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**STAT 101 Dr. Kari Lock Morgan**

Synthesis Big Picture Essential Synthesis Review Speed Dating

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**Final Monday, April 28th, 2 – 5pm No make-ups, no excuses**

30% of your course grade Cumulative from the entire course Open only to a calculator and 3 double-sided pages of notes prepared only by you

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**Help Before Final Wednesday, 4/23: Thursday, 4/24: Friday, 4/25:**

3 – 4pm, Prof Morgan, Old Chem 216 4 – 9pm, Stat Ed Help, Old Chem 211A Thursday, 4/24: 5 – 7pm, Yating, Old Chem 211A Friday, 4/25: 1 – 3pm, Prof Morgan, Old Chem 216 3 – 4 pm, REVIEW SESSION, room tbd Sunday, 4/27: 4 – 6pm, Tori, Old Chem 211A 6 – 7pm, Stat Ed Help, Old Chem 211A 7 – 9pm, David, Old Chem 211A Monday, 4/28: 12:30 – 1:30, Prof Morgan, Old Chem 216

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**Review What is Bayes Rule?**

A way of getting from P(A if B) to P(B if A) A way of calculating P(A and B) A way of calculating P(A or B)

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Data Collection The way the data are/were collected determines the scope of inference For generalizing to the population: was it a random sample? Was there sampling bias? For assessing causality: was it a randomized experiment? Collecting good data is crucial to making good inferences based on the data

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**Exploratory Data Analysis**

Before doing inference, always explore your data with descriptive statistics Always visualize your data! Visualize your variables and relationships between variables Calculate summary statistics for variables and relationships between variables – these will be key for later inference The type of visualization and summary statistics depends on whether the variable(s) are categorical or quantitative

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Estimation For good estimation, provide not just a point estimate, but an interval estimate which takes into account the uncertainty of the statistic Confidence intervals are designed to capture the true parameter for a specified proportion of all samples A P% confidence interval can be created by bootstrapping (sampling with replacement from the sample) and using the middle P% of bootstrap statistics

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Hypothesis Testing A p-value is the probability of getting a statistic as extreme as observed, if H0 is true The p-value measures the strength of the evidence the data provide against H0 “If the p-value is low, the H0 must go” If the p-value is not low, then you can not reject H0 and have an inconclusive test

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**p-value A p-value can be calculated by**

A randomization test: simulate statistics assuming H0 is true, and see what proportion of simulated statistics are as extreme as that observed Calculating a test statistic and comparing that to a theoretical reference distribution (normal, t, 2, F)

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**Hypothesis Tests Variables Appropriate Test One Quantitative**

Single mean (t) One Categorical Single proportion (normal) Chi-square Goodness of Fit Two Categorical Difference in proportions (normal) Chi-square Test for Association One Quantitative, Difference in means (t) Matched pairs (t) ANOVA (F) Two Quantitative Correlation (t) Slope in Simple Linear Regression (t) More than two Multiple Regression (t, F)

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Regression Regression is a way to predict one response variable with multiple explanatory variables Regression fits the coefficients of the model The model can be used to Analyze relationships between the explanatory variables and the response Predict Y based on the explanatory variables Adjust for confounding variables

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Probability

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**Romance Do these variables differ for males and females?**

What variables help to predict romantic interest? Do these variables differ for males and females? All we need to figure this out is DATA! (For all of you, being almost done with STAT 101, this is the case for many interesting questions!)

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Speed Dating We will use data from speed dating conducted at Columbia University, 276 males and 276 females from Columbia’s various graduate and professional schools Each person met with people of the opposite sex for 4 minutes each After each encounter each person said either “yes” (they would like to be put in touch with that partner) or “no”

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**Speed Dating Data What are the cases?**

Students participating in speed dating Speed dates Ratings of each student

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**Speed Dating What is the population? Ideal population?**

More realistic population?

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Speed Dating It is randomly determined who the students will be paired with for the speed dates. We find that people are significantly more likely to say “yes” to people they think are more intelligent. Can we infer causality between perceived intelligence and wanting a second date? Yes No

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Successful Speed Date? What is the probability that a speed date is successful (results in both people wanting a second date)? To best answer this question, we should use Descriptive statistics Confidence Interval Hypothesis Test Regression Bayes Rule

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Successful Speed Date? 63 of the 276 speed dates were deemed successful (both male and female said yes). A 95% confidence interval for the true proportion of successful speed dates is (0.2, 0.3) (0.18, 0.28) (0.21, 0.25) (0.13, 0.33)

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Pickiness and Gender Are males or females more picky when it comes to saying yes? Guesses? Males Females

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Pickiness and Gender Yes No Males 146 130 Females 127 149 Are males or females more picky when it comes to saying yes? How could you answer this? Test for a single proportion Test for a difference in proportions Chi-square test for association ANOVA Either (b) or (c)

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Pickiness and Gender Do males and females differ in their pickiness? Using α = 0.05, how would you answer this? a) Yes b) No c) Not enough information

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Reciprocity Male says Yes Male says No Female says Yes 63 64 Female says No 83 66 Are people more likely to say yes to someone who says yes back? How would you best answer this? Descriptive statistics Confidence Interval Hypothesis Test Regression Bayes Rule

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Reciprocity Male says Yes Male says No Female says Yes 63 64 Female says No 83 66 Are people more likely to say yes to someone who says yes back? How could you answer this? Test for a single proportion Test for a difference in proportions Chi-square test for association ANOVA Either (b) or (c)

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Reciprocity Are people more likely to say yes to someone who says yes back? p-value = Based on this data, we cannot determine whether people are more likely to say yes to someone who says yes back.

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**Race and Response: Females**

Does the chance of females saying yes to males differ by race? How could you answer this question? Test for a single proportion Test for a difference in proportions Chi-square goodness of fit Chi-square test for association ANOVA Asian Black Caucasian Latino Other 0.50 0.57 0.42 0.48 0.53

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**Race and Response: Males**

Each person rated their date on a scale of based on how much they liked them overall. Does how much males like females differ by race? How would you test this? Chi-square test t-test for a difference in means Matched pairs test ANOVA Either (b) or (d)

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**Physical Attractiveness**

Each person also rated their date from 1-10 on the physical attractiveness. Do males rate females higher, or do females rate males higher? Which tool would you use to answer this question? Two-sample difference in means Matched pair difference in means Chi-Square ANOVA Correlation

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**Physical Attractiveness**

The histogram shown is of the data bootstrap distribution randomization distribution sampling distribution 𝑥 𝑀 − 𝑥 𝐹 =0.406 95% CI: (0.10, 0.71) p-value =0.01

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Other Ratings Each person also rated their date from 1-10 on the following attributes: Attractiveness Sincerity Intelligence How fun the person seems Ambition Shared interests Which of these best predict how much someone will like their date?

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Multiple Regression MALES RATING FEMALES: FEMALES RATING MALES:

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Ambition and Liking Do people prefer their dates to be less ambitious??? How does the perceived ambition of a date relate to how much the date is liked? How would you answer this question? Inference for difference in means ANOVA Inference for correlation Inference for simple linear regression Either (b), (c) or (d)

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**Simple Linear Regression**

MALES RATING FEMALES: FEMALES RATING MALES:

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**Ambition and Liking r = 0.44, SE = 0.05 Find a 95% CI for .**

Test whether 1 differs from 0.

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**ALL YOU NEED IS DATA!!! After taking STAT 101: Thank You!!!**

If you have a question that needs answering… ALL YOU NEED IS DATA!!! Thank You!!!

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Objectives 10.1 Simple linear regression

Objectives 10.1 Simple linear regression

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