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 A government which robs Peter
to pay Paul can  always depend
on the support of Paul
.
George B. Shaw

 

snarrinstitute 

 

The Snarr Institute

 

All instructional videos that are available on The Snarr Institute are organized below by course: Principles of Macroeconomics, Business Quantitative Methods (statistics), and Labor Economics. An example is shown below. In this video, I analyze the economics of Hurricane Katrina (at minute 0:00), math teacher shortages (15:20), minimum wage hikes (22:30), immigration (26:50), and Lebron James's pay (31:00).
 


 
My son can be heard in the background leaping off the couch.

 

 

 

 

 

Principles of Macroeconomics

 

 

 

 

minute

 

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Lecture 1 (1 of 3)—Demand & Supply basics

 

 

 

 

 

Deriving Big Mac demand

1:40

 

 

 

 

Deriving oil supply

12:10

 

 

 

 

Law of demand and supply

23:54

 

 

 

Lecture 1 (2 of 3)—Applying the law of demand & supply

 

 

 

 

 

Economics of Hurricane Katrina

0:00

 

 

 

 

Economics of the math teacher shortage

15:20

 

 

 

 

Economics of minimum wage hikes

22:30

 

 

 

 

Economics of immigration

26:50

 

 

 

 

Economics of Lebron James pay

31:00

 

 

 

Lecture 1 (3 of 3)—Applications of the PPF

 

 

 

 

 

Economics of health care reform

1:20

 

 

 

 

Economics of budget deficits

14:00

 

 

 

 

A linear model of free trade

26:10

 

 

 

 

 

 

 

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Lecture 2 (1 of 3)—Inflation

 

 

 

 

 

Consumer Price Index (CPI)

0:56

 

 

 

 

Price level or cost of the market basket

3:35

 

 

 

 

Inflation rate

11:10

 

 

 

 

Cost of living adjustment (COLA)

16:34

 

 

 

 

Real price

26:00

 

 

 

 

Real wage

28:25

 

 

 

 

Real interest rate

38:10

 

 

 

Lecture 2 (2 of 3)—Economic growth

 

 

 

 

 

Nominal GDP

0:23

 

 

 

 

Real GDP

12:18

 

 

 

 

Economic growth rate

15:24

 

 

 

 

Stand of living

20:08

 

 

 

 

Business cycle

23:15

 

 

 

 

Determination of GDP and price level

24:20

 

 

 

 

AD fluctuations and changes in price level and GDP

24:40

 

 

 

Lecture 2 (3 of 3)—Unemployment

 

 

 

 

 

Unemployment

0:00

 

 

 

 

Natural rate of unemployment

3:34

 

 

 

 

High unemployment in a recessionary gap

9:43

 

 

 

 

Low unemployment in an inflationary gap

12:22

 

 

 

 

Phillips Curve

19:36

 

 

 

 

Augmented Phillips Curve and natural rate of unemployment

21:32

 

 

 

 

 

 

 

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Lecture 3Consumer, Import, & Aggregate Expenditure

 

 

 

 

 

Snarrian Keynesian consumption

7:24

 

 

 

 

Snarrian consumption

16:18

 

 

 

 

iMport function

20:22

 

 

 

 

Snarrian aggregate expenditure

23:21

 

 

 

 

Keynesian equilibrium

29:20

 

 

 

 

Government spending multiplier

34:10

 

 

 

 

Tax cut multiplier

38:10

 

 

 

 

 

 

 

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Lecture 4 (1 of 2)Aggregate Demand & Potential GDP

 

 

 

 

 

Deriving aggregate demand from aggregate expenditure

0:00

 

 

 

 

Government spending stimulus

9:23

 

 

 

 

Tax cut stimulus

11:40

 

 

 

 

Full employment output and the economy's production function

15:35

 

 

 

Lecture 4 (2 of 2)—Aggregate Supply & AD-AS-Yfe Equilibrium

 

 

 

 

 

Snarrian aggregate supply

2:15

 

 

 

 

Inflationary gap

14:04

 

 

 

 

Recessionary gap

21:58

 

 

 

 

Induced inflationary gap

29:00

 

 

 

 

 

 

 

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Lecture 5 (1 of 4)—Fiscal Policy

 

 

 

 

 

Budget surplus/deficit

0:00

 

 

 

 

Financing budget deficits with government bonds

1:28

 

 

 

 

Mock Treasury auction

4:27

 

 

 

 

Keynesian expansionary fiscal policy

8:39

 

 

 

Lecture 5 (2 of 4)—Fiscal Policy

 

 

 

 

 

Keynesian expansionary fiscal policy (continued)

0:00

 

 

 

 

Keynesian restrictive fiscal policy

1:18

 

 

 

 

Keynesian intervention and the crowding out effect

4:05

 

 

 

 

New classical view of fiscal policy

4:53

 

 

 

 

New classical view of fiscal policy and the loanable funds market

7:37

 

 

 

 

Timing of Keynesian fiscal policy

9:24

 

 

 

Lecture 5 (3 of 4)—Fiscal Policy

 

 

 

 

 

Timing of Keynesian fiscal policy (continued)

0:00

 

 

 

 

Applying the Augmented Phillips Curve

6:18

 

 

 

 

Automatic stabilizers

9:30

 

 

 

 

A modern synthesis of fiscal policy

11:49

 

 

 

 

Supply-side fiscal policy

12:20

 

 

 

Lecture 5 (4 of 4)—Fiscal Policy

 

 

 

 

 

Laffer curve

0:00

 

 

 

 

Supply-side tax cuts

3:55

 

 

 

 

Percent of federal income tax paid by each income group

6:33

 

 

 

 

History of US fiscal policy

10:20

 

 

 

 

Economic growth vs. the growth rate of tax revenue

12:38

 

 

 

 

Fiscal policy impotent?

13:37

 

 

 

 

 

 

 

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Lecture 6 (1 of 6)—Monetary Policy

 

 

 

 

 

Definition and history of money (0:00),

0:00

 

 

 

 

from the GOLD standard to the GOD standard (3:45),

3:45

 

 

 

 

money demand and supply model (4:52)

4:52

 

 

 

Lecture 6 (2 of 6)—Monetary Policy

 

 

 

 

 

Definition of money

0:00

 

 

 

 

hyperinflation

2:17

 

 

 

 

 

Hyperinflation video 1

 

 

 

 

 

 

Hyperinflation video 2

 

 

 

 

 

 

Hyperinflation video 3

 

 

 

 

 

hyperinflation in Germany

6:20

 

 

 

 

hyperinflation in Yugoslavia

8:50

 

 

 

 

banks

10:44

 

 

 

Lecture 6 (3 of 6)—Monetary Policy

 

 

 

 

 

Money creation via increased bank lending

0:00

 

 

 

 

Money destruction via decreased bank lending

3:19

 

 

 

 

The Snarrian model of demand in the federal funds market

5:00

 

 

 

 

A numerical example of Snarrian demand in the federal funds market

11:57

 

 

 

Lecture 6 (4 of 6)—Monetary Policy

 

 

 

 

 

Supply in the federal funds market

0:00

 

 

 

 

A numerical example of supply

3:45

 

 

 

 

Federal funds market equilibrium in normal mode

4:49

 

 

 

 

An example of federal funds market equilibrium in normal mode

6:40

 

 

 

 

Raising the discount rate in normal mode

8:50

 

 

 

 

Raising the required reserve ratio & emergency mode

10:16

 

 

 

 

Raising the required reserve ratio and its effect on money supply

13:17

 

 

 

 

Raising the required reserve ratio and its effect on the AD-AS model

13:30

 

 

 

Lecture 6 (5 of 6)—Monetary Policy

 

 

 

 

 

Open Market Purchase (OMP)

0:00

 

 

 

 

Economics of an OMP in normal mode

1:59

 

 

 

 

The effect of an OMP on money supply

3:15

 

 

 

 

The effect of an OMP on the AD-AS model

3:38

 

 

 

 

Applying monetary policy using the Augmented Phillips Curve

5:00

 

 

 

 

Open Market Sale, summarized

6:32

 

 

 

 

The financial crisis, interest on reserves, and crisis mode

7:43

 

 

 

 

Unwinding the Fed's balance sheet

9:56

 

 

 

Lecture 6 (6 of 6)—Keynes vs. Hayek

 

 

 

 

 

Politicians side with Keynes not freedom's friend, Hayek

0:00

 

 

 

 

A tree analogy of Keynes vs. Hayek

5:22

 

 

 

 

The forest management analogy of Keynes vs. Hayek

6:35

 

 

 

 

Economics of the Keynes vs. Hayek debate

7:26

 

 

 

 

The flat Keynesian Bush-Obama recovery

8:58

 

 

 

 

The Hayek trajectory that would not be

9:38

 

 

 

 

 

Fear the Boom and Bust

 

 

 

 

 

 

Fight of the Century: Keynes vs. Hayek Round Two

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Business Quantitative Methods

 

 

 

 

minute

 

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Chapter 1—Introduction to Business Statistics

0:00

 

 

 

 

 

 

 

 

Chapter 2—Tabular and Graphical data summary

 

 

 

 

 

Categorical data

0:00

 

 

 

 

Frequency distribution

1:00

 

 

 

 

Relative frequency table

2:50

 

 

 

 

Percent frequency

4:28

 

 

 

 

Bar char

4:39

 

 

 

 

Pie chart

6:01

 

 

 

 

Quantitative data

7:48

 

 

 

 

Frequency example

8:20

 

 

 

 

Histogram

15:00

 

 

 

 

Cumulative frequency distribution

18:39

 

 

 

 

Ogive

23:07

 

 

 

 

Crosstabulation (or pivot) tables

25:42

 

 

 

 

Row and column percentages of a crosstabulation table

30:12

 

 

 

 

Simpson's Paradox

36:40

 

 

 

 

scatterplots

48:13

 

 

 

 

 

 

 

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Chapter 3 (1 of 3)—Central tendency

 

 

 

 

 

Introduction to summarizing quantitative data

0:00

 

 

 

 

Population mean

3:00

 

 

 

 

Sample mean

6:27

 

 

 

 

Median

10:09

 

 

 

 

Mode

14:32

 

 

 

 

Percentiles

17:05

 

 

 

 

Quartiles

21:36

 

 

 

Chapter 3 (2 of 3)—Variation

 

 

 

 

 

Variation in data

0:00

 

 

 

 

Range

2:18

 

 

 

 

Interquartile range

2:42

 

 

 

 

Population variance

3:46

 

 

 

 

Biased sample variance

6:12

 

 

 

 

Sample variance

7:43

 

 

 

 

Population vs sample standard deviation

7:58

 

 

 

 

Coefficient of variation

9:06

 

 

 

 

Sample standard deviation example

10:46

 

 

 

Chapter 3 (3 of 3)—Distributional Shape and Correlation

 

 

 

 

 

introduction of distributional shape and correlation

0:00

 

 

 

 

z-score

0:21

 

 

 

 

Skewness and histograms

7:35

 

 

 

 

Chebyshev's Theorem

15:47

 

 

 

 

Empirical Rule

17:21

 

 

 

 

Outliers

18:16

 

 

 

 

Using z-scores to verify Chebyshev's Theorem or the Empirical Rule

19:28

 

 

 

 

Correlation and covariance

22:18

 

 

 

 

Population covariance vs sample covariance

23:09

 

 

 

 

Scatterplot

22:41

 

 

 

 

Correlation

28:54

 

 

 

 

 

 

 

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Chapter 4 (1 of 2)—Introduction to probability

 

 

 

 

 

Definition of probability 

0:00

 

 

 

 

Experiment and sample space 

2:07

 

 

 

 

Product rule 

5:05

 

 

 

 

Tree diagram 

6:54

 

 

 

 

Counting rule 1-order matters, replacement 

10:13

 

 

 

 

Counting rule 2-order does not matter, no replacement 

16:51

 

 

 

 

Counting rule 3-order matters, no replacement 

20:59

 

 

 

 

Assigning probabilities 

23:52

 

 

 

 

Classical probabilities 

24:33

 

 

 

 

Relative frequency probabilities

27:29

 

 

 

 

Subjective probabilities 

30:30

 

 

 

 

Events 

36:14

 

 

 

Chapter 4 (2 of 2)—More on probability

 

 

 

 

 

Probability rules 

0:00

 

 

 

 

Complement rule

1:19

 

 

 

 

Intersection of non-mutually exclusive events

2:55

 

 

 

 

Intersection of mutually exclusive events 

4:41

 

 

 

 

Union of non-mutually exclusive events 

6:47

 

 

 

 

Union of mutually exclusive events 

9:13

 

 

 

 

Conditional probability 

11:27

 

 

 

 

Intersection of dependent events 

15:05

 

 

 

 

Independent events 

17:17

 

 

 

 

Intersection of independent events 

17:44

 

 

 

 

Bayes' Theorem and drug testing 

20:25

 

 

 

 

 

 

 

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Chapter 5 (1 of 3)—Discrete Probability Distributions

 

 

 

 

 

Discrete vs continuous random variables

0:00

 

 

 

 

Numerical example of discrete PDF 

6:09

 

 

 

 

Expected value of discrete variable 

8:53

 

 

 

 

Standard deviation of discrete variable 

10:46

 

 

 

 

Uniform discrete PDF 

13:18

 

 

 

 

Binomial PDF

19:48

 

 

 

Chapter 5 (2 of 3)—Discrete Probability Distributions

 

 

 

 

 

Numerical example of Binomial PDF

0:00

 

 

 

 

Using Binomial PDF to compute Pick Three Lottery 

11:58

 

 

 

 

Expected winnings of Pick Three Lottery 

13:37

 

 

 

 

Poisson PDF

14:40

 

 

 

Chapter 5 (3 of 3)—Discrete Probability Distributions

 

 

 

 

 

Hypergeometric vs Binomial 

0:00

 

 

 

 

Hypergeometric PDF

0:58

 

 

 

 

Numerical example of Hypergeometric PDF

3:25

 

 

 

 

Hypergeometric and PowerBall Lottery 

10:55

 

 

 

 

Expected winnings of PowerBall Lottery

14:21

 

 

 

 

 

 

 

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Chapter 6—Continuous Probability Distributions

 

 

 

 

 

Continuous vs. Discrete Probability Distributions 

0:00

 

 

 

 

Uniform continuous distribution 

2:53

 

 

 

 

Expected value and variance of uniform continuous distribution 

4:49

 

 

 

 

Normal distribution 

8:18

 

 

 

 

Changing the mean of a normal distribution 

10:00

 

 

 

 

Changing the standard deviation of a normal distribution 

10:32

 

 

 

 

Standard normal distribution 

11:07

 

 

 

 

Verifying the Empirical Rule 

12:42

 

 

 

 

Using the standard normal table to find probabilities 

13:41

 

 

 

 

Using the standard normal table to find z values 

27:43

 

 

 

 

Standardizing non-standard normal distributions 

31:48

 

 

 

 

Numerical example of non-standard normal distribution 

32:39

 

 

 

 

Exponential probability distribution 

39:31

 

 

 

 

 

 

 

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Chapter 7—Sampling Distribution

 

 

 

 

 

Difference between a distribution and sampling distribution 

0:00

 

 

 

 

Sampling error 

1:18

 

 

 

 

Example: census versus a sample 

2:25

 

 

 

 

Population mean and proportion 

3:24

 

 

 

 

Population standard deviation 

4:51

 

 

 

 

Simple sampling 

7:33

 

 

 

 

Sample mean and proportion 

11:31

 

 

 

 

Sample standard deviation 

12:33

 

 

 

 

Comparing population parameters and sample statistics 

15:12

 

 

 

 

Sampling distribution of the mean 

17:03

 

 

 

 

Standard error of the mean 

17:54

 

 

 

 

Normality of the sampling distribution of the mean

19:10

 

 

 

 

Numerical example of sampling distribution 

19:50

 

 

 

 

Numerical example of the Law of Large Numbers 

26:01

 

 

 

 

Sampling distribution of the proportion 

28:27

 

 

 

 

Intuition of the standard error of the proportion 

29:12

 

 

 

 

Normality of the sampling distribution of the proportion 

34:27

 

 

 

 

Numerical example of sampling distribution of the proportion 

34:54

 

 

 

 

 

 

 

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Chapter 8—Confidence Intervals

 

 

 

 

 

Intuition of confidence interval 

0:00

 

 

 

 

Interval Estimate (IE) of mu with sigma known 

2:58

 

 

 

 

Normality conditions of IE with sigma known 

11:55

 

 

 

 

Numerical example of IE with sigma known 

12:36

 

 

 

 

The sigma unknown case 

16:01

 

 

 

 

t distribution 

16:45

 

 

 

 

t and standard normal are identical for large samples 

17:52

 

 

 

 

Finding t values in the t table 

19:22

 

 

 

 

IE of mu with sigma unknown 

21:31

 

 

 

 

Numerical example of IE with sigma unknown 

30:03

 

 

 

 

Conditions needed to use t-distribution 

32:19

 

 

 

 

Necessary sample size for IE of mu 

33:29

 

 

 

 

IE of the proportion 

36:46

 

 

 

 

Normality condition 

37:02

 

 

 

 

Example of IE of proportion 

38:34

 

 

 

 

Necessary sample size for IE of proportion

40:39

 

 

 

 

 

 

 

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Chapter 9 (1 of 5)—Hypothesis testing involving one mean

 

 

 

 

 

Definition of null and alternative hypotheses

2:34

 

 

 

 

Example of developing null and alternative hypotheses

4:40

 

 

 

 

Type I and II errors

6:54

 

 

 

 

One-tail test with known variance

8:56

 

 

 

 

Determining the p value with known variance

11:28

 

 

 

Chapter 9 (2 of 5)—Hypothesis testing involving one mean

 

 

 

 

 

Determining the p value (continued)

0:00

 

 

 

 

Determining z critical values with known variance

4:23

 

 

 

Chapter 9 (3 of 5)—Hypothesis testing involving one mean

 

 

 

 

 

One tail hypothesis test involving one mean with known variance

0:00

 

 

 

 

Two tail hypothesis test involving one mean with known variance

4:12

 

 

 

Chapter 9 (4 of 5)—Hypothesis testing involving one mean

 

 

 

 

 

Hypothesis testing with unknown variance

0:00

 

 

 

 

A numerical example of hypothesis testing with unknown variance

2:58

 

 

 

 

A numerical example of a two tail test with unknown variance

6:50

 

 

 

Chapter 9 (5 of 5)—Hypothesis testing involving one mean

 

 

 

 

 

Hypothesis testing with unknown variance

0:00

 

 

 

 

Hypothesis tests about a population proportion

4:27

 

 

 

 

A numerical example of a hypothesis tests about a population proportion

5:56

 

 

 

 

 

 

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Chapter 10 (1 of 5)—Hypothesis testing involving two means

 

 

 

 

 

The sampling distribution of the mean

2:18

 

 

 

 

The sampling distribution of the differences in means

4:08

 

 

 

 

Confidence interval for the difference in means

4:40

 

 

 

 

A numerical example with known variances

6:48

 

 

 

Chapter 10 (2 of 5)—Hypothesis testing involving two means

 

 

 

 

 

One tail test with known populations variances

0:00

 

 

 

 

Deriving the test statistic from the confidence interval

3:08

 

 

 

 

A numerical example of a one tail test, known variances

5:12

 

 

 

 

Testing when population, unknown variances

10:30

 

 

 

 

A numerical example a confidence interval, unknown variances

12:27

 

 

 

Chapter 10 (3 of 5)—Hypothesis testing involving two means

 

 

 

 

 

Example a confidence interval, unknown variances (continued)

0:00

 

 

 

 

Example hypothesis testing, unknown variances

5:07

 

 

 

 

Example matched samples, unknown variances

9:33

 

 

 

Chapter 10 (4 of 5)—Hypothesis testing involving two means

 

 

 

 

 

Example matched samples, unknown variances (continued)

0:00

 

 

 

 

Sampling distribution of a difference in proportions

5:52

 

 

 

 

Example of interval estimate of a difference in proportions

8:25

 

 

 

Chapter 10 (5 of 5)—Hypothesis testing involving two means

 

 

 

 

 

Example one tail test involving two proportions

0:00

 

 

 

 

Example two tail test involving two proportions

4:53

 

 

 

 

 

 

 

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Chapter 11 (1 of 3)—Hypothesis testing involving one and two variances

 

 

 

 

 

Sampling distribution of chi-square statistic

0:00

 

 

 

 

Finding chi-square critical values

6:18

 

 

 

 

Deriving the interval estimate of the population variance

10:02

 

 

 

 

Example of confidence interval of the population variance

12:31

 

 

 

Chapter 11 (2 of 3)—Hypothesis testing involving one and two variances

 

 

 

 

 

Example of confidence interval of the population variance (continued)

0:00

 

 

 

 

Hypothesis tests about a population variance

4:43

 

 

 

 

Example of one tail test of population variance

5:28

 

 

 

 

Hypothesis tests about two population variance

8:34

 

 

 

 

Deriving F-stat

8:57

 

 

 

Chapter 11 (3 of 3)—Hypothesis testing involving one and two variances

 

 

 

 

 

Example of test involving two population variances

0:00

 

 

 

 

 

 

 

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Chapter 12 (1 of 4)—Goodness of fit and Independence tests

 

 

 

 

 

Goodness of fit test

0:00

 

 

 

 

Independence tests

8:00

 

 

 

Chapter 12 (2 of 4)—Goodness of fit and Independence tests

 

 

 

 

 

Independence tests - continued

0:00

 

 

 

 

Goodness of fit test for Normal Distribution

8:14

 

 

 

Chapter 12 (3 of 4)—Goodness of fit and Independence tests

 

 

 

 

 

Example: Goodness of fit test for Normal Distribution

0:00

 

 

 

Chapter 12 (4 of 4)—Goodness of fit and Independence tests

 

 

 

 

 

Example: Goodness of fit test for Poisson Distribution

0:00

 

 

 

 

 

 

 

 

 

Chapter 13 (1 of 3)—Analysis Of Variance (ANOVA)

 

 

 

 

 

Use ANOVA to test equality of three or more means

0:00

 

 

 

 

 Completely randomized design

5:02

 

 

 

Chapter 13 (2 of 3)—Analysis Of Variance (ANOVA)

 

 

 

 

 

Completely randomized block design (continued)

0:00

 

 

 

 

Example of completely randomized block design

6:30

 

 

 

Chapter 13 (3 of 3)—Analysis Of Variance (ANOVA)

 

 

 

 

 

Example of completely randomized block design (continued)

0:00

 

 

 

 

Randomized block design

1:14

 

 

 

 

 

 

 

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Chapter 14 (1 of 5)—Simple Regression

 

 

 

 

 

Regression equation vs. estimated regression equation

0:00

 

 

 

 

Residual versus error

6:50

 

 

 

 

Least squares method

12:21

 

 

 

 

Slope coefficient vs. correlation

13:33

 

 

 

 

Shortcut to compute slope coefficient

14:15

 

 

 

Chapter 14 (2 of 5)—Simple Regression

 

 

 

 

 

Simple regression example

0:00

 

 

 

 

Sample means

0:52

 

 

 

 

Sample variances

1:19

 

 

 

 

Covariance

5:40

 

 

 

 

Compute coefficient b1

7:03

 

 

 

 

Compute the intercept

7:29

 

 

 

 

Forming the predicted equation

8:09

 

 

 

 

SST, SSR, SSE, R square, and correlation

10:37

 

 

 

Chapter 14 (3 of 5)—Simple Regression

 

 

 

 

 

Scatterplot y vs. x

0:00

 

 

 

 

Graphing the predicted equation

0:38

 

 

 

 

Computing R square, SST, SSE, and SSR

2:54

 

 

 

 

Interpreting the R square

11:19

 

 

 

 

Square root of R square equals the correlation

11:58

 

 

 

Chapter 14 (4 of 5)—Simple Regression

 

 

 

 

 

Assumptions of the error

0:00

 

 

 

 

Verifying the assumptions

3:07

 

 

 

 

Computing predicted values and residuals

5:00

 

 

 

 

Standard deviation of the residuals

6:26

 

 

 

 

Leverage

7:23

 

 

 

 

Standardized residual plot

13:19

 

 

 

Chapter 14 (5 of 5)—Simple Regression

 

 

 

 

 

Testing for significance

0:00

 

 

 

 

Distribution of coefficient b1

0:59

 

 

 

 

Coefficient significance test

3:07

 

 

 

 

Interpreting coefficient b1 - t test

4:05

 

 

 

 

Test of model significance - F test

5:05

 

 

 

 

ANOVA table

7:32

 

 

 

Prediction and confidence intervals

8:38

 

 

 

 

 

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Chapter 15 (1 of 8)—Multiple Regression Analysis

 

 

 

 

 

Definition of multiple regression 0:00

0:00

 

 

 

 

Research question and specifying the model 3:35

3:35

 

 

 

 

Building testable hypotheses using theory in graphs 6:33

6:33

 

 

 

 

Building testable hypotheses using constrained utility optimization

11:45

 

 

 

Chapter 15 (2 of 8)—Multiple Regression Analysis

 

 

 

 

 

Descriptive statistics

0:00

 

 

 

 

Scatterplots of y versus the independent variables

8:42

 

 

 

 

Correlations & multicollinearity versus omitted variable bias

10:11

 

 

 

 

Computing regression coefficients using sample statistics

13:39

 

 

 

Chapter 15 (3 of 8)—Multiple Regression Analysis

 

 

 

 

 

Computing regression coefficients using sample statistics

0:00

 

 

 

 

Predicted equation, y-hat, residuals, SSE, SSR, & SST

2:03

 

 

 

 

Coefficient significance test

5:49

 

 

 

 

Omitted variable bias in simple regression

8:57

 

 

 

 

Interpreting coefficient

9:42

 

 

 

 

egression output table

12:12

 

 

 

 

Interpreting R-square

14:12

 

 

 

Chapter 15 (4 of 8)—Multiple Regression Analysis

 

 

 

 

 

Computing regression coefficients using sample statistics

0:00

 

 

 

 

Predicted equation, y-hats, residuals, SSE, SSR, and SST

3:01

 

 

 

 

Coefficient significance test

7:55

 

 

 

 

Interpreting the coefficient of a logged independent variable

10:51

 

 

 

 

Omitted variable bias in simple regression

14:26

 

 

 

Chapter 15 (5 of 8)—Multiple Regression Analysis

 

 

 

 

 

Omitted variable bias in simple regression (continued)

0:00

 

 

 

 

Why multicollinearity presents a problem for Excel

2:40

 

 

 

 

Reducing omitted variable bias

3:41

 

 

 

 

Forming the predicted equation

7:22

 

 

 

 

Error assumptions

8:33

 

 

 

 

Residuals are estimated errors and verifying the assumptions

9:00

 

 

 

 

Test for zero mean of the error

11:00

 

 

 

Chapter 15 (6 of 8)—Multiple Regression Analysis

 

 

 

 

 

Testing for heteroscedasticity

0:00

 

 

 

 

Testing for normality

6:42

 

 

 

Chapter 15 (7 of 8)—Multiple Regression Analysis

 

 

 

 

 

Testing for normally (continued)

0:00

 

 

 

 

Autocorrelation test

5:10

 

 

 

 

Testing for linearity

9:43

 

 

 

 

F test of model significance

11:53

 

 

 

Chapter 15 (8 of 8)—Multiple Regression Analysis

 

 

 

 

 

F test of model significance (continued)

0:00

 

 

 

 

Coefficient significance tests

4:04

 

 

 

 

Interpreting coefficients of variables and logged variables

9:08

 

 

 

 

 

 

 

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Regression Analysis in Excel (1 of 7)

 

 

 

 

 

Descriptive statistics & scatterplots of the variables

0:00

 

 

 

Regression Analysis in Excel Part (2 of 7)

 

 

 

 

 

Using Excel's regression package to estimate a linear equation

0:00

 

 

 

 

Forming the predicted equation

2:20

 

 

 

 

Computing predicted values and residuals by hand

3:01

 

 

 

 

Interpreting R square

5:03

 

 

 

 

Testing coefficients for significance

6:00

 

 

 

 

Testing for model significance

7:12

 

 

 

 

Residuals are used to test the error assumptions

7:39

 

 

 

Regression Analysis in Excel (3 of 7)

 

 

 

 

 

Test errors have a mean equal to zero (0:00

0:00

 

 

 

 

Test linearity of model

0:51

 

 

 

Regression Analysis in Excel (4 of 7)

 

 

 

 

 

Testing errors have constant variance (homoscedasticity)

0:00

 

 

 

 

White's test

2:43

 

 

 

Regression Analysis in Excel Part (5 of 7)

 

 

 

 

 

Test normality of the errors

0:00

 

 

 

Regression Analysis in Excel (6 of 7)

 

 

 

 

 

Testing autocorrelation in the errors

0:00

 

 

 

Regression Analysis in Excel (7 of 7)

 

 

 

 

 

Testing for significance in the coefficients and model

0:00

 

 

 

 

Interpreting the coefficients

4:20

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Labor Economics

 

 

 

 

minute

 

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Labor Economics lecture 1 (1 of 2)—Introduction

 

 

 

 

 

Labor market participants

0:00

 

 

 

 

Why is there a shortage of high school mathematics teachers?

1:12

 

 

 

 

Minimum wage hikes and unemployment

10:30

 

 

 

 

Immigration and its affect on wages

18:44

 

 

 

 

Using multiple regression to confirm theory

30:18

 

 

 

 

Model specification

32:34

 

 

 

 

Using economic theory in graphs to build a testable hypothesis

35:05

 

 

 

 

Using economic theory and calculus to build a testable hypothesis

41:49

 

 

 

 

Summarizing data

45:05

 

 

 

 

Correlations and multicollinearity

52:57

 

 

 

 

Log variable transformation

56:03

 

 

 

 

Ordinary least squares

57:20

 

 

 

Labor Economics lecture 1 (2 of 2)—Introduction

 

 

 

 

 

Excel's simple regression output

0:00

 

 

 

 

Interpreting the R square in simple regression

0:38

 

 

 

 

Omitted variable bias

1:53

 

 

 

 

Excel's multiple regression output

2:40

 

 

 

 

Interpreting the R square in multiple regression

3:03

 

 

 

 

Forming the predicted equation

4:10

 

 

 

 

Computing residuals

5:40

 

 

 

 

Heteroskedasticity

9:56

 

 

 

 

Autocorrelation

16:20

 

 

 

 

Linearity

21:48

 

 

 

 

Normality

24:18

 

 

 

 

Test of model significance

31:08

 

 

 

 

Test of coefficient significance

34:04

 

 

 

 

Coefficient interpretation

42:22

 

 

 

 

 

 

 

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Labor Economics lecture 2—Worker utility and labor supply

 

 

 

 

 

Utility: more consumption and leisure is better

0:00

 

 

 

 

Law of Diminishing Marginal Utility

1:41

 

 

 

 

Solving for C yields the indifference curve

5:08

 

 

 

 

Graphing indifference curve U = 10

6:40

 

 

 

 

Graphing indifference curve U = 20

7:45

 

 

 

 

Budget line constructed

9:13

 

 

 

 

Graphing the budget line

11:30

 

 

 

 

Shifting the budget line after non-earned income

14:17

 

 

 

 

Rotating the budget line after wages rise

15:18

 

 

 

 

Utility maximization

16:41

 

 

 

 

Utility max after an increase in non-earned income

19:40

 

 

 

 

Utility max with increase in wage and substitution effect dominant

22:04

 

 

 

 

Utility maximization with increase in wage and income effect dominant

23:36

 

 

 

 

Income effect dominates

24:24

 

 

 

 

Substitution effect dominates

27:06

 

 

 

 

Reservation wage

28:29

 

 

 

 

Backward bending labor supply and labor supply elasticity

33:21

 

 

 

 

Application: the benefit reduction rate

39:05

 

 

 

 

Application: Earned Income Tax Credit

49:46

 

 

 

 

 

 

 

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Labor Economics lecture 3—Firm production and labor demand

 

 

 

 

 

Long run production function

0:00

 

 

 

 

Short run production function

1:27

 

 

 

 

Law of Diminishing Marginal Productivity

3:48

 

 

 

 

Graphing total and marginal product of labor

5:56

 

 

 

 

Short run output, expenses, revenue, and profit equations

7:20

 

 

 

 

Short run profit maximization rule using graphs

10:14

 

 

 

 

Short run profit maximization rule using calculus

12:00

 

 

 

 

Short run labor demand equation

16:11

 

 

 

 

Graphing the labor demand equation

17:03

 

 

 

 

Shifting short run labor demand

19:15

 

 

 

 

Short run profit maximization rule

21:54

 

 

 

 

Long run cost minimization

22:40

 

 

 

 

Solving long run production for K yields the isoquant

22:56

 

 

 

 

Graphing isoquants

24:09

 

 

 

 

Isocost

25:53

 

 

 

 

Graphing isocost

26:54

 

 

 

 

Rising cost shifts isocost

28:03

 

 

 

 

Rising price of K rotates isocost

29:01

 

 

 

 

Rising wage rotates isocost

31:19

 

 

 

 

Long run cost minimization

33:15

 

 

 

 

Long run labor demand and the output effect of falling wage

38:41

 

 

 

 

Scale and substitution effects

42:28

 

 

 

 

Elasticity of substitution and curvature of the isoquant

45:57

 

 

 

 

Application: the free market punishes firms that discriminate

47:00

 

 

 

 

Application: affirmative action can work

48:06

 

 

 

 

Application: affirmative action can put firms out of business

49:29

 

 

 

 

Perfect substitutes and perfect complements in production

54:48

 

 

 

 

 

 

 

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Labor Economics lecture 4 (1 of 2)—Labor market equilibrium

 

 

 

 

 

Competitive labor market equilibrium

0:00

 

 

 

 

Minimum wage creates unemployment

2:18

 

 

 

 

Cobweb web model

5:13

 

 

 

 

Long run dynamics and data observation

13:33

 

 

 

 

Pareto efficient

17:17

 

 

 

 

Producer surplus

19:32

 

 

 

 

Worker surplus

20:31

 

 

 

 

The competitive market maximizes total surplus

21:10

 

 

 

 

Payroll taxes assessed on firms

22:09

 

 

 

 

Payroll taxes assessed on employees

28:55

 

 

 

 

Payroll taxes assessed on firms and employees

34:22

 

 

 

 

Payroll taxes assessed on firms with inelastic labor supply

37:39

 

 

 

 

Employment subsidies paid to firms

40:46

 

 

 

Labor Economics lecture 4 (2 of 2)—Labor market equilibrium

 

 

 

 

 

Immigration equalizes wages in two different regions

0:00

 

 

 

 

Evidence of wage equalization across regions

6:26

 

 

 

 

Immigrants and natives are perfect substitutes

9:39

 

 

 

 

Immigrants and natives are perfect complements

14:07

 

 

 

 

Evidence of wage being associated with immigration

15:41

 

 

 

 

Immigration is Pareto efficient

18:40

 

 

 

 

Perfectly discriminating monopsonist

20:46

 

 

 

 

Perfectly nondiscriminating monopsonist

25:35

 

 

 

 

Perfectly nondiscriminating monopolist

29:10