Analytics for Crammers - Courses for Crammers

Cram Kit Content (Part 1)

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What is a population distribution? 1:00

Normal Distribution 0:42

Empirical Rule 2:40

Where the Empirical Rule falls short 1:15

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What is a sampling distribution? 1:15

Standard Error (SE) 2:37

Central Limit Theorem 1:51

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Z-distribution 0:15

How to use z-scores 1:05

Z-scores with population mean 1:56

Z-scores with sample mean 1:47

Z-scores with population proportions aren’t necessary! 0:31

Z-scores with sample proportion 2:13

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Why are t-scores necessary? 1:24

T-distributions 1:24

How to use t-scores 1:05

T-scores with sample mean 2:12

T-scores with sample proportions are pointless! 1:58

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Why do we need assumptions? 0:30

Assumptions for Sample Means 0:48

Assumptions for Sample Proportions 0:52

Assumptions for Two Means 0:53

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What are confidence intervals? 1:16

Confidence… not probability! 1:11

Margin of Error 1:12

Critical Values 1:35

Confidence interval with one-proportion z-test 1:39

Confidence interval with one-mean z-test 1:41

Confidence interval with one-mean t-test 1:50

Confidence interval with two-mean t-test 1:28

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What is a hypothesis test? 1:30

Null hypothesis 0:36

Alternative hypothesis 0:52

One-tail tests 1:03

Two-tail tests 0:57

Alpha level 1:07

Concluding statement 0:55

Type I Error 0:43

Type II Error 0:49

Hypothesis test with one-proportion z-test 2:42

Hypothesis test with one-mean z-test 2:52

Hypothesis test with one-mean t-test 3:20

Hypothesis test with two-mean t-test 3:04

Cram Kit Content (Part 2)

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Why are Chi Square tests necessary? 0:44

Chi Square Distribution 0:49

Chi Square – Goodness of Fit 1:45

Chi Square – Independence 1:42

Assumptions for Chi Square – Goodness of Fit 0:25

Assumptions for Chi Square – Independence 0:20

Hypothesis test with Chi Square – Goodness of Fit 2:26

Hypothesis test with Chi Square – Independence 2:36

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Correlation (r) 1:22

Residuals 0:53

Line of Best Fit 0:33

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What is simple linear regression? 2:00

Y-intercept 0:42

Coefficients 0:52

Assumptions for Linear Regression 1:03

Extrapolation 0:41

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Degrees of freedom (regression) 0:57

Sum of Squares Error (SSE) 1:22

Sum of Squares Regression (SSR) 1:33

Sum of Squares Total (SST) 1:23

Mean Square Error (MSE) 1:24

F Ratio 1:18

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R-squared (Coefficient of Determination) 3:37

Adjusted R-squared 1:37

Root Mean Square Error (RMSE) 1:20

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t ratios 1:24

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Why use confidence intervals with linear regression? 0:31

Confidence intervals with coefficient 2:06

Confidence intervals with predicted mean response 3:06

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Prediction vs. confidence 0:32

Prediction interval with individual observation 2:49

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Hypothesis test with coefficient 2:16

Hypothesis test with entire model 1:54

Cram Kit Content (Part 3)

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What is multiple linear regression? 1:47

Dummy variables 3:01

How to compare models 0:48

Confidence intervals with coefficient 2:06

Hypothesis test with coefficient 2:16

Hypothesis test with entire model 1:54

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What is a time series? 0:19

Trend component 0:36

Seasonal component 0:31

Cyclical component 0:32

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Simple Moving Average 2:19

Exponential Smoothing 2:10

Estimated vs. predicted 0:34

Mean Square Error (MSE) with time series 0:39

Mean Absolute Deviation (MAD) 1:03

Mean Absolute Percentage Error (MAPE) 1:02

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Additive Model 0:49

Multiplicative Model 1:18

Dummy variables for seasonal / cyclical components 1:31

Cram Kit Content (Part 4)

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Marginal probabilities 0:59

Joint probabilities 0:52

Conditional probabilities 1:39

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How to create a probability tree 1:02

P(A | B) with probability tree 0:40

P(A and B) with probability tree 1:39

Bayes Theorem 1:33

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Support 0:35

Confidence 0:39

Lift Ratio 1:52