Class Notes for Statistics at University of Pennsylvania (UPENN)


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UPENNSTAT 101Richard WatermanWinter

STAT 101 Lecture Notes - Lecture 23: Mean Squared Error

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

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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

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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

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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 Notes - Lecture 24: Market Saturation, Diminishing Returns

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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

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UPENNSTAT 101Richard WatermanWinter

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

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UPENNSTAT 101Richard WatermanWinter

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

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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

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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 16: Statistical Inference, Normal Distribution

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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 11: Independent And Identically Distributed Random Variables, Chocolate Chip Cookie, Bernoulli Distribution

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Stat 101 - introduction to business statistics - lecture 11: random variables. The bernoulli r. v. is the simplest type of random variable you a can ge
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UPENNSTAT 101Richard WatermanWinter

STAT 101 Lecture Notes - Lecture 8: Conditional Probability, Main Diagonal, Sample Space

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Stat 101 - introduction to business statistics - lecture 8: continuing probability. In some problems, the object of interest is the chance of two event
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