### Create a StudySoup account

#### Be part of our community, it's free to join!

Already have a StudySoup account? Login here

# PRIN OF STATISTICS STAT 101

ISU

GPA 3.5

### View Full Document

## 4

## 0

## Popular in Course

## Popular in Statistics

This 20 page Class Notes was uploaded by Giovani Ullrich PhD on Saturday September 26, 2015. The Class Notes belongs to STAT 101 at Iowa State University taught by W. Stephenson in Fall. Since its upload, it has received 4 views. For similar materials see /class/214396/stat-101-iowa-state-university in Statistics at Iowa State University.

## Reviews for PRIN OF STATISTICS

### What is Karma?

#### Karma is the currency of StudySoup.

#### You can buy or earn more Karma at anytime and redeem it for class notes, study guides, flashcards, and more!

Date Created: 09/26/15

0 Who Cans of cola o What Weight of contents units grams When 2000 0 Where In an apartment in Ames How For each can of cola the unopened can is weighed using a scale that measures to the nearest gram The can is opened and the contents are consumed The can is rinsed and allowed to dry The empty can is then weighed on the same scale The di erence in weight full can minus empty can is the weight of the contents Why To investigate the weight grams and its relationship with volume cans of cola are labeled as containing 355 milliliters 368 351 355 367 352 369 370 369 370 355 354 357 366 353 373 365 355 356 362 354 353 373 368 349 0 Stem plot 33 33 34 34 35 35 36 36 37 37 38 38 0 Stem plot split stem 34 34 35 35 36 36 37 37 Who Countries of the world What Life expectancy at birth years Wealth index a function of per capita gross domestic product When 2004 Where The Central Intelligence Agency Why The CIA collects information on countries in the world to track changes in those countries How I m not sure as the CIA is pretty secretive about how it collects data Country Country Index France Predicted Life Expectancy 241 77lWealth Index Life Expectancy 5 6 7 8 9 10 11 Wealth Index 1d 10 39 I 39 I E 5 I I 3 l o 5 5 10 I 39139 I I I 6 7 8 9 Wealth Index Stat 101L Lecture 32 J m Li l Population t Shape Not normal skewed right tCenter Mean M 808 Spread Standard Deviation 039 622 Distribution of y an aShape Approximately normal Center Mean 808 ISpread Standard Deviation a 7 622 7 SDy W if 7 278 Stat 101L Lecture 32 Population t Shape Not normal skewed right tCenter Mean M 808 Spread Standard Deviation 039 622 Distribution of i an 25 aShape Approximately normal Center Mean 808 tSpread Standard Deviation 039 7 622 SDy W 7E 124 Stat 101L Lecture 32 Central Limit Theorem When selecting random samples from a population With a distribution that is not normal the distribution of y Will be approximately normally distributed a The larger the sample the better the approximation Conditions Random sampling condition Samples must be selected at random from the population at 10 condition When sampling Without replacement the sample size should be less than 10 of the population size Summary Distribution of y Shape Approximately normal Center y Spread SDG n Stat 101L Lecture 1 What is Statistics OStatistics is a way of reasoning about the world around us OStatistics helps us use data to make informed decisions Statistics in a Word Statistics is about variation The world is full of data Data exhibit variation Recognizing displaying and quantifying variation in data can help us make sense of the world Try to explain variation Statistics in the News 9Decrease in gonorrhea rates credited to high beer taxes OStudy links suicide country music OPro wrestling tied to dating violence Stat 101L Lecture 1 Statistics in the News OTM lessens brain s pain response OCaffeine reduces skin cancer risks study shows Physical Sciences Medicine ii STATISTICS is used in What will I learn in this class 0 How to summarize sample data 0 How to interpret data summaries 0 How to differentiate between observational studies and experiments 0 How to design and carry out a sampling study an effective experiment 0 How to make statistically valid inferences about a population 0 How to use the computer to analyze data Stat 101L Lecture 2 Data 9 lnformati on 9Context is important Who are we collectng data on 0 Cases Rows in a data table What data are we collecting Variables Columns in a data table Acacia bonarienrir Moist 159 35 59 94 Dendrupanaxarbureus Moist 146 25 31 56 Heliumrpusamerimnm Moist 236 30 40 70 Margaritarianobizir Moist 134 24 23 47 Puuren39amacruphylla Moist 155 57 46 103 Buugainvillea madam Dry 219 12 12 24 Chrysuphyllumgunumrpun Dry 142 59 70 129 Jacaran39asp Dry 212 21 50 71 Phyllusrylunrhamnuides Dry 149 13 21 39 Sweetia urimsa Dry 170 28 26 54 Data OWho Tropical treesshrub s OWhat Species type of forest Average crown exposure sugar mgg starch mgg nonstructural carbohydrate mgg Stat 101L Lecture 2 Population 7 all items of interest E 16 Au eey bsin sugar concentration tropical forests Statistic r numerical summary o are sam 19 Example sample mean sugar concentration What OVariables Categorical Qualitative variable 0 Species Type of forest Nume1ical Quantitative variable 0 Crown exposure 0 Sugar starch and NCH Categorical ONominal names Species Acacia bonariensis 90rdinal ordered categories Forest type Dry Moist ordered by amount of wetness Stat 101L Lecture 2 Categorical In the next chapter we Will see how to summarize categorical data by counting how many cases are in each category Do not confuse the summary of a categorical variable With a numerical quantitative variable Categorical 9What type of forest a treeshrub is in is a categorical variable 9 Summary There are 5 Moi st forest treesshrubs and 5 Wet forest trees shrub s Numerical ODiscrete takes on only certain isolated values Crown exposure 1 2 3 4 or 5 9Continuous measurement Sugar concentration any value greater than 0 mgg Stat 101L Lecture 20 Randomization Randomization tends to spread the effects of uncontrolled outside variables evenly across the treatment groups Randomization reduces the chance that an uncontrolled outside variable Will bias the results Replication Within an experiment must have several experimental units in each treatment group can assess the natural variation in the response for units treated the same Replication as Repeating the entire experiment This is especially important if the subjects in an experiment are not a random sample from a population Are the results of the entire experiment repeatable Stat 101L Lecture 20 Diagram Group 1 subjects gt Treatment 1 Compare Subjects random warren Response Group 2 gt Treatment 2 several subjects Block There may be attributes of the experimental units that can t be controlled but may affect the response as Group similar experimental units into blocks and then randomize the assignment of treatments Within each block Blocking gt Math ability Very high high average low and very low assign at random students from each math ability group to each treatment Stat 101L Lecture 20 More Ideas Control treatment its Blinding Singleblind and double blind gt Placebos Multiple Factors Factors can use calculator yes no 7 can use a formula sheet yes no as Treatments 7calculator and formulas calculator but no formulas formulas but no calculator no calculator and no formulas Confounding xi Sodium and blood pressure 7 All subjects on the low sodium diet had their blood pressure measured by a registered nurse using a standard manual cuff and stethoscope 7 All subjects on the high sodium diet had their blood pressure measured using an automated cuff and digital readout Stat 101L Lecture 3 Categorical Data 0 National Opinion Research Center s General Social Survey 0 In 1996 a sample of 1895 adults in the US were asked the question When is premarital sex wrong The participants were also asked with what religion they were affiliated Who W hat 0 Who A sample of 1895 adults 0 What Opinion on premarital sex Religious affiliation What 0 When is premarital sex wrong Categorical Always Almost Always Sometimes Never 0 What is your religious af liation Categorical Catholic Jewish Protestant None Other Stat 101L Lecture 3 Data Table Adult Answer Religion 1 Never Catholic 2 Always Protestant 3 Never Jewish 4 Sometimes None When is Premarital Sex Wrong Class Count Always 452 2385 Almost Always 183 966 Sometimes 429 2264 Never 831 4385 Total 1895 100 Bar Chart Cnunl t Always 4Neer snmeumes 21mm mwaw Stat 101L Lecture 3 05u 40 au g E 02u S mu 9 g g g Mmuw 1 wa 2MmustAMay 35mm ANever When is Premarital Sex Wrong Almost Never Total Stat 101L Lecture 3 When is Premarital Sex Wrong Almost Never Total Mosaic Plot Cahahl mm om Pmtesmm Nara Rehgmn Ammde 1 A ways 2 A mos A ways SSome mes 4Never mm M Wpwm JMP Output for Cooling Coffee Time min Temp F 0 1806 5 1717 8 1645 11 1594 15 1512 18 1461 22 1401 25 1350 30 1291 34 1220 38 1165 42 1121 45 1080 50 1027 Simple Linear Regression of Temperature on Time 1W 5 180 39 439 39 39 170 339 39 C160 239 3150 g 139 I U E140 a 0 I 1 39 l 130 120 2 I 39 I 3 110 4 l um I l I I I I 5 I I I I I I 0 0 10 20 30 40 5o 60 0 0 10 20 30 40 so Time min Time min Linear Fit Predicted Temp F 17668943 15587521Time min Summary of Fit RSquare 0991257 RSquare Adj 0990528 Root Mean Square Error 2410473 Mean of Response 1385 Observations or Sum Wgts 14 Parameter Estimates Term Estimate Std Error t Ratio Pr0bgt1t1 Intercept 17668943 1219428 14490 lt0001 Time min 1558752 004226 3689 lt0001 Simple Linear Regression of LogTemp on Time 5 0010 54 I 5393quot 0005 I 52 351 g 39 I E 5 3 0000 5 D 3 49 391 39 4339 0005 I 39 47 46 45 I I I I I I 410quot 39 39 39 39 39 39 0 0 10 20 30 40 50 60 0 0 10 20 30 40 50 Time min Time min Linear Fit Predicted LogTemp 5194561 7 00113736Time min Summary of Fit RSquare 0999314 RSquare Adj 0999257 Root Mean Square Error 0004907 Mean of Response 4915909 Observations or Sum Wgts 14 Analysis of Variance Source DF Sum of Squares Mean Square F Ratio Model 1 042086624 0420866 1747745 Error 12 000028897 0000024 Prob gt F C Total 13 042115521 lt0001 Parameter Estimates Term Estimate Std Error t Ratio Probgt1t1 Intercept 5194561 0002482 2092 5 lt 0001 Time min 0011374 0000086 1322 lt0001 Fit Special with LogTemperature on Time 190 15 180 10 170 39 39 A160 05 V150 39 39 Q E 140 0390 DC I 130 05 120 39 110 10 1 5 0 0 10 20 30 40 50 60 0 5 1390 2390 3390 Time min Time min Transformed Fit Log Predicted L0gTemp F 5194561 00113736Time min Summary of Fit on Log Scale RSquare 0999314 RSquare Adj 0999257 Root Mean Square Error 0004907 Mean of Response 4915909 Observations or Sum Wgts 14 Parameter Estimates Term Estimate Std Error t Ratio Pr0bgt1t1 Intercept 5194561 0002482 20925 lt0001 Time min 0011374 0000086 1322 lt0001 Prediction Equation on Original Scale Predicted Temp F 1803e 00114T lt iquotgt Fit Measured on Original Scale Sum of Squared Error 57605521 Root Mean Square Error 06928535 RSquare 09992777 Sum of Residuals 00815943

### BOOM! Enjoy Your Free Notes!

We've added these Notes to your profile, click here to view them now.

### You're already Subscribed!

Looks like you've already subscribed to StudySoup, you won't need to purchase another subscription to get this material. To access this material simply click 'View Full Document'

## Why people love StudySoup

#### "There's no way I would have passed my Organic Chemistry class this semester without the notes and study guides I got from StudySoup."

#### "Selling my MCAT study guides and notes has been a great source of side revenue while I'm in school. Some months I'm making over $500! Plus, it makes me happy knowing that I'm helping future med students with their MCAT."

#### "There's no way I would have passed my Organic Chemistry class this semester without the notes and study guides I got from StudySoup."

#### "It's a great way for students to improve their educational experience and it seemed like a product that everybody wants, so all the people participating are winning."

### Refund Policy

#### STUDYSOUP CANCELLATION POLICY

All subscriptions to StudySoup are paid in full at the time of subscribing. To change your credit card information or to cancel your subscription, go to "Edit Settings". All credit card information will be available there. If you should decide to cancel your subscription, it will continue to be valid until the next payment period, as all payments for the current period were made in advance. For special circumstances, please email support@studysoup.com

#### STUDYSOUP REFUND POLICY

StudySoup has more than 1 million course-specific study resources to help students study smarter. If you’re having trouble finding what you’re looking for, our customer support team can help you find what you need! Feel free to contact them here: support@studysoup.com

Recurring Subscriptions: If you have canceled your recurring subscription on the day of renewal and have not downloaded any documents, you may request a refund by submitting an email to support@studysoup.com

Satisfaction Guarantee: If you’re not satisfied with your subscription, you can contact us for further help. Contact must be made within 3 business days of your subscription purchase and your refund request will be subject for review.

Please Note: Refunds can never be provided more than 30 days after the initial purchase date regardless of your activity on the site.