Class Notes for Richard Waterman


UPENNSTAT 101Richard WatermanWinter

STAT 101 Lecture Notes - Lecture 24: Market Saturation, Diminishing Returns

26
Stat 101 - introduction to business statistics - lecture 24: regression. Define r 2 as (r) 2 , that is the sample correlation squared. It is sometimes
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UPENNSTAT 101Richard WatermanWinter

STAT 101 Lecture 25: Curvature

33
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UPENNSTAT 101Richard WatermanWinter

STAT 101 Lecture Notes - Lecture 23: Mean Squared Error

32
Stat 101 - introduction to business statistics - lecture 23: line of fit. Mathematically leverage the equation: derivatives and optimizations. The best
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UPENNSTAT 101Richard WatermanWinter

STAT 101 Lecture 22: AB Testing

33
Stat 101 - introduction to business statistics - lecture 22: a/b testing. Making decisions based on what you know, not just a hunch. It has been around
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UPENNSTAT 101Richard WatermanWinter

STAT 101 Lecture Notes - Lecture 21: Confidence Interval, Statistical Unit, Null Hypothesis

27
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UPENNSTAT 101Richard WatermanWinter

STAT 101 Lecture Notes - Lecture 20: Linear Combination, Null Hypothesis, Confounding

23
Stat 101 - introduction to business statistics - lecture 20: comparative analytics. Here we are dealing with the population sample paradigm again, but
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UPENNSTAT 101Richard WatermanWinter

STAT 101 Lecture Notes - Lecture 18: Confidence Interval, Sample Size Determination, Odds Ratio

24
Stat 101 - introduction to business statistics - lecture 18: interpreting confidence. 95% of intervals created according to this procedure are expected
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UPENNSTAT 101Richard WatermanWinter

STAT 101 Lecture Notes - Lecture 19: Null Hypothesis, Test Statistic, Sample Size Determination

44
Stat 101 - introduction to business statistics - lecture 19: hypothesis testing (continued) Set up the appropriate null and alternative hypothesis. Com
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UPENNSTAT 101Richard WatermanWinter

STAT 101 Lecture Notes - Lecture 17: Confidence Interval, Sample Size Determination, Sampling Distribution

19
Stat 101 - introduction to business statistics - lecture 17: confidence interval for. The reason why the t is different from a z is that the s in the d
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UPENNSTAT 101Richard WatermanWinter

STAT 101 Lecture Notes - Lecture 16: Statistical Inference, Normal Distribution

22
Stat 101 - introduction to business statistics - lecture 16: confidence intervals. Giving a range of numbers as an estimate as opposed to a single poin
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UPENNSTAT 101Richard WatermanWinter

STAT 101 Lecture Notes - Lecture 23: Mean Squared Error

32
Stat 101 - introduction to business statistics - lecture 23: line of fit. Mathematically leverage the equation: derivatives and optimizations. The best
View Document
UPENNSTAT 101Richard WatermanWinter

STAT 101 Lecture Notes - Lecture 12: Central Limit Theorem, Random Variable, Standard Deviation

33
Stat 101 - introduction to business statistics - lecture 12: the normal distribution. The shape of the distribution (bell curve) It is characterized by
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UPENNSTAT 101Richard WatermanWinter

STAT 101 Lecture Notes - Lecture 19: Null Hypothesis, Test Statistic, Sample Size Determination

44
Stat 101 - introduction to business statistics - lecture 19: hypothesis testing (continued) Set up the appropriate null and alternative hypothesis. Com
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UPENNSTAT 101Richard WatermanWinter

STAT 101 Lecture Notes - Lecture 7: Sample Space, Set Notation, Data Center

45
Stat 101 - introduction to business statistics - lecture 7: intro to probability. Set-up: there is an event about which, we"d like to make a probabilit
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UPENNSTAT 101Richard WatermanWinter

STAT 101 Lecture 22: AB Testing

33
Stat 101 - introduction to business statistics - lecture 22: a/b testing. Making decisions based on what you know, not just a hunch. It has been around
View Document
UPENNSTAT 101Richard WatermanWinter

STAT 101 Lecture Notes - Lecture 17: Confidence Interval, Sample Size Determination, Sampling Distribution

19
Stat 101 - introduction to business statistics - lecture 17: confidence interval for. The reason why the t is different from a z is that the s in the d
View Document
UPENNSTAT 101Richard WatermanWinter

STAT 101 Lecture Notes - Lecture 14: Sampling Frame, Simple Random Sample, Standard Deviation

23
Stat 101 - introduction to business statistics - lecture 14: sampling. You can"t survey an entire population, and so you can survey a sample represenat
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UPENNSTAT 101Richard WatermanWinter

STAT 101 Lecture Notes - Lecture 15: Hospital-Acquired Infection, Standard Deviation, Decision Rule

24
Stat 101 - introduction to business statistics - lecture 15: quality control. To understand quality control chart, you must master: central limit theor
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UPENNSTAT 101Richard WatermanWinter

STAT 101 Lecture Notes - Lecture 9: Contingency Table

32
Stat 101 - introduction to business statistics - lecture 9: bayes theorem. You want to find the probability of cancer given a positive screening test.
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UPENNSTAT 101Richard WatermanWinter

STAT 101 Lecture Notes - Lecture 10: Random Variable, Squared Deviations From The Mean, S&P 500 Index

43
Stat 101 - introduction to business statistics - lecture 10: random variables & A random variable is a variable whose value is not known with certainty
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UPENNSTAT 101Richard WatermanWinter

STAT 101 Lecture Notes - Lecture 24: Market Saturation, Diminishing Returns

26
Stat 101 - introduction to business statistics - lecture 24: regression. Define r 2 as (r) 2 , that is the sample correlation squared. It is sometimes
View Document
UPENNSTAT 101Richard WatermanWinter

STAT 101 Lecture 25: Curvature

33
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