### Create a StudySoup account

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

Already have a StudySoup account? Login here

# Basic Statistical Reasoning 2.25.16 Exam Study Guide MATH 15500

IC

GPA 3.3

### View Full Document

## About this Document

## 149

## 0

## Popular in Basic Statistical Reasoning

## Popular in Department

This 14 page Study Guide was uploaded by neatnotesfrschool on Wednesday February 24, 2016. The Study Guide belongs to MATH 15500 at Ithaca College taught by Doris G Wolfgramm in Spring 2016. Since its upload, it has received 149 views.

## Similar to MATH 15500 at IC

## Popular in Subject

## Reviews for Basic Statistical Reasoning 2.25.16 Exam Study Guide

### 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: 02/24/16

Key: 2.24.16Exam Study Guide Equations Wednesday, February 1:40 PM16 Vocab Other Vocab ► Chapter 1- Where Do Data Come From? Vocabulary Individuals: Objects you're collecting data on (people, animals, things, etc.); also called subjects, elements, or units; who/what is being studied Variable: A characteristic of an individual; can change or fluctuate • Qualitative/categorical- non numerical, cannot be expressed in numbers (ex: eye color, and occupation) • Quantitative- numerical, can be expressed in numbers (ex: age, height, and weight) Observational Study: A researcher observes and records data but does not influence the response in any way Population:Entire group of individuals, not restricted to people, varies in size but is usually large • Goal is to get info (data) about population without studying the whole population Sample: Subgroup of a population; collect data from the sample- only thing studied • The result is applied to the population • Goal is to make conclusions about the population from the sample data Census: You study the whole population, no small samples • Disadvantages are that it takes a while and is expensive Experiment: Deliberately imposes some treatment on individuals in order to observe their responses • Goal is to study whether the treatment causes a change in the response → If it does then there is a cause + effect relationship ► Chapter 2- Samples, Good and Bad Key: 2.24.16Exam Study Guide Equations Wednesday, February 1:40 PM16 Vocab Other Vocab ► Chapter 1- Where Do Data Come From? Vocabulary Individuals: Objects you're collecting data on (people, animals, things, etc.); also called subjects, elements, or units; who/what is being studied Variable: A characteristic of an individual; can change or fluctuate • Qualitative/categorical- non numerical, cannot be expressed in numbers (ex: eye color, and occupation) • Quantitative- numerical, can be expressed in numbers (ex: age, height, and weight) Observational Study: A researcher observes and records data but does not influence the response in any way Population:Entire group of individuals, not restricted to people, varies in size but is usually large • Goal is to get info (data) about population without studying the whole population Sample: Subgroup of a population; collect data from the sample- only thing studied • The result is applied to the population • Goal is to make conclusions about the population from the sample data Census: You study the whole population, no small samples • Disadvantages are that it takes a while and is expensive Experiment: Deliberately imposes some treatment on individuals in order to observe their responses • Goal is to study whether the treatment causes a change in the response → If it does then there is a cause + effect relationship ► Chapter 2- Samples, Good and Bad → If it does then there is a cause + effect relationship ► Chapter 2- Samples, Good and Bad Vocabulary Convenience Sampling: People in the sample don't represent the entire population, so it's unreliable information (ex: shopping mall) Voluntary Response Sample: Call or write in polls; not representative of the whole population; attracts polarized opinions; unscientific Simple Random Sample (SRS): Most popular sampling technique; reliable random information about the population;unbiased and scientific How to pick a random sample 1. Make or obtain a list of the population to be sampled (alphabetical order) 2. Labeling- number each entry on the list; number of digits in each label must match the number of digits in the population 3. Enter the random numbers table- use random start; identify the line (row to get started) 4. Identify the first 5-digit block of numbers; separate block into 2-digit numbers 5. Select 5 numbers between 01-30 in order to satisfy the sample size; an number between 01-30 is in the sample (ignore the rest); each number can only be picked for one sample ► Chapter 3- What Do Samples Tell Us? Vocabulary Parameter: Population number; actual number in the population; fixed number that's usually unknown (unless we do a census) • We can estimate an unknown parameter using a sample Statistic: Sample statistic; a sample number • Look for "survey/ed, polled, opinion poll, and sampled" → If it does then there is a cause + effect relationship ► Chapter 2- Samples, Good and Bad Vocabulary Convenience Sampling: People in the sample don't represent the entire population, so it's unreliable information (ex: shopping mall) Voluntary Response Sample: Call or write in polls; not representative of the whole population; attracts polarized opinions; unscientific Simple Random Sample (SRS): Most popular sampling technique; reliable random information about the population;unbiased and scientific How to pick a random sample 1. Make or obtain a list of the population to be sampled (alphabetical order) 2. Labeling- number each entry on the list; number of digits in each label must match the number of digits in the population 3. Enter the random numbers table- use random start; identify the line (row to get started) 4. Identify the first 5-digit block of numbers; separate block into 2-digit numbers 5. Select 5 numbers between 01-30 in order to satisfy the sample size; an number between 01-30 is in the sample (ignore the rest); each number can only be picked for one sample ► Chapter 3- What Do Samples Tell Us? Vocabulary Parameter: Population number; actual number in the population; fixed number that's usually unknown (unless we do a census) • We can estimate an unknown parameter using a sample Statistic: Sample statistic; a sample number • Look for "survey/ed, polled, opinion poll, and sampled" we do a census) • We can estimate an unknown parameter using a sample Statistic: Sample statistic; a sample number • Look for "survey/ed, polled, opinion poll, and sampled" Variability: How spread out the values of the sample statistic are when we take many samples; large variability means that the result of sampling is not repeatable • A good sampling method has both small bias and small variability P-hat: Sample proportion- percentage of the sample who favor Number is a statistic, used to estimate the unknown "p" • Steps in sampling 1. Define the population 2. Define the parameter 3. Determine sample size a. Take a random sample b. Ask them, analyze the sample c. Calculate the sample statistic -hat p-hat= number of people who say yes/favor ___________________________________ sample size 4. Report result on the news 5. Calculate the margin of error 1 ________________________ = answer x 100 (square root) sample size **Margin of error is always reported as +/- 6. Construct a confidence interval (range of numbers that tells you where the parameter is located) we do a census) • We can estimate an unknown parameter using a sample Statistic: Sample statistic; a sample number • Look for "survey/ed, polled, opinion poll, and sampled" Variability: How spread out the values of the sample statistic are when we take many samples; large variability means that the result of sampling is not repeatable • A good sampling method has both small bias and small variability P-hat: Sample proportion- percentage of the sample who favor Number is a statistic, used to estimate the unknown "p" • Steps in sampling 1. Define the population 2. Define the parameter 3. Determine sample size a. Take a random sample b. Ask them, analyze the sample c. Calculate the sample statistic -hat p-hat= number of people who say yes/favor ___________________________________ sample size 4. Report result on the news 5. Calculate the margin of error 1 ________________________ = answer x 100 (square root) sample size **Margin of error is always reported as +/- 6. Construct a confidence interval (range of numbers that tells you where the parameter is located) (square root) sample size **Margin of error is always reported as +/- 6. Construct a confidence interval (range of numbers that tells you where the parameter is located) P-hat - margin of error Sample proportion + margin of error ^^ Confidence Interval **Any number in the confidence interval could be the parameter/has a 95% chance of being the parameter (outside is 5%) 7. State confidence level (how sure you are that the parameter is in the confidence interval: 95%) • Always assume 95% if no confidence is stated 8. Confidence statement ->Types of errors in estimating the parameter 1. Variability (sampling variability) a. You want the sample size to be a close estimate 2. Bias a. Systematic flaw in your sample that cannot be corrected b. Result: you will get a bad estimate **cannot increase sample size to minimize EX: convenience sample size, voluntary response samples Margin of error and sample size Large sample size -> Small margin Small sample size -> Large margin **can be opposite directions To cut the margin of error in hamultiply sample size by 4 ► Chapter 4- Sample Surveys in the Real World Sampling errors (square root) sample size **Margin of error is always reported as +/- 6. Construct a confidence interval (range of numbers that tells you where the parameter is located) P-hat - margin of error Sample proportion + margin of error ^^ Confidence Interval **Any number in the confidence interval could be the parameter/has a 95% chance of being the parameter (outside is 5%) 7. State confidence level (how sure you are that the parameter is in the confidence interval: 95%) • Always assume 95% if no confidence is stated 8. Confidence statement ->Types of errors in estimating the parameter 1. Variability (sampling variability) a. You want the sample size to be a close estimate 2. Bias a. Systematic flaw in your sample that cannot be corrected b. Result: you will get a bad estimate **cannot increase sample size to minimize EX: convenience sample size, voluntary response samples Margin of error and sample size Large sample size -> Small margin Small sample size -> Large margin **can be opposite directions To cut the margin of error in halmultiply sample size by 4 ► Chapter 4- Sample Surveys in the Real World Sampling errors ► Chapter 4- Sample Surveys in the Real World Sampling errors 1. Random sampling error: difference between the sample statistic and the parameter 2. Taking a convenience/voluntary response sample: bad sampling method, don't use it 3. Undercoverage: miss certain groups while taking a sample; using an incomplete sampling frame (ex: phonebook) Non-sampling errors 1. Processing error: clerical mistakes, typing errors 2. Response error: subjects lie to you, so you record the wrong data 3. Nonresponse: subjects can't be contacted or refuse to answer 4. Question wording: the way you ask a question effects the response Stratified sample:sampling from a list • Divide population into subgroups= strata • Take a random sample of subgroups • This lowers the margin of error ► Chapter 5- Experiment, Good and Bad Vocabulary Response variable (dependent): variable that is being effected or changed by another variable (effect) Explanatory variable (independent): variable that is believed to explain or cause change in the response variable (cause) Randomized comparative experiment 1. Start out with a group of subject who are similar 2. Subjects are divided into two roughly equal groups: experimental + control 3. Selection of who goes into which group is based on random assignment using a random number table ► Chapter 4- Sample Surveys in the Real World Sampling errors 1. Random sampling error: difference between the sample statistic and the parameter 2. Taking a convenience/voluntary response sample: bad sampling method, don't use it 3. Undercoverage: miss certain groups while taking a sample; using an incomplete sampling frame (ex: phonebook) Non-sampling errors 1. Processing error: clerical mistakes, typing errors 2. Response error: subjects lie to you, so you record the wrong data 3. Nonresponse: subjects can't be contacted or refuse to answer 4. Question wording: the way you ask a question effects the response Stratified sample:sampling from a list • Divide population into subgroups= strata • Take a random sample of subgroups • This lowers the margin of error ► Chapter 5- Experiment, Good and Bad Vocabulary Response variable (dependent): variable that is being effected or changed by another variable (effect) Explanatory variable (independent): variable that is believed to explain or cause change in the response variable (cause) Randomized comparative experiment 1. Start out with a group of subject who are similar 2. Subjects are divided into two roughly equal groups: experimental + control 3. Selection of who goes into which group is based on random assignment using a random number table Randomized comparative experiment 1. Start out with a group of subject who are similar 2. Subjects are divided into two roughly equal groups: experimental + control 3. Selection of who goes into which group is based on random assignment using a random number table 4. Goal is to create minimum of two groups that are similar --------------------------------------------------------------------------------- 5. Experimental groups get a treatment called the explanatory variable 6. Control groups get treatment called a placebo (which is a fake) 7. Subjects don't know which group they're in 8. Researchers then examine each group and compare findings Example: Explanatory variable= hydro Response variable= pain (from sickle-cell anemia) **Create as many groups as you have explanatory variables Mixed group of volunteers (299) Group 1 (152): treatment Random assignment explanatory variable hydro Group 2 (147): treatment placebo End -> compare pain episodes (25 each) **If the outcome is the same, assume the medicine doesn't work Complications while experimenting 1. Placebo effect: people believe they've received the explanatory variable (psychological) • To prevent, use enough subjects in your experiment 2. Lurking variables: un-expected variables that interact with the explanatory and response variables, and mess up cause + effect relationships (ex: Advil and other pain relievers) ► Chapter 6-Experiment in the Real World Randomized comparative experiment 1. Start out with a group of subject who are similar 2. Subjects are divided into two roughly equal groups: experimental + control 3. Selection of who goes into which group is based on random assignment using a random number table 4. Goal is to create minimum of two groups that are similar --------------------------------------------------------------------------------- 5. Experimental groups get a treatment called the explanatory variable 6. Control groups get treatment called a placebo (which is a fake) 7. Subjects don't know which group they're in 8. Researchers then examine each group and compare findings Example: Explanatory variable= hydro Response variable= pain (from sickle-cell anemia) **Create as many groups as you have explanatory variables Mixed group of volunteers (299) Group 1 (152): treatment Random assignment explanatory variable hydro Group 2 (147): treatment placebo End -> compare pain episodes (25 each) **If the outcome is the same, assume the medicine doesn't work Complications while experimenting 1. Placebo effect: people believe they've received the explanatory variable (psychological) • To prevent, use enough subjects in your experiment 2. Lurking variables: un-expected variables that interact with the explanatory and response variables, and mess up cause + effect relationships (ex: Advil and other pain relievers) ► Chapter 6-Experiment in the Real World ► Chapter 6-Experiment in the Real World Vocabulary Double-blind experiment: neither the subjects nor the people who work with them known which treatment each subject is receiving Nonadherers: subjects who participate but don't follow the experimental treatment; can cause bias Completely randomized design: experimental design; all the subjects are allocated at random among all the treatments ► Chapter 6-Experiment in the Real World Vocabulary Double-blind experiment: neither the subjects nor the people who work with them known which treatment each subject is receiving Nonadherers: subjects who participate but don't follow the experimental treatment; can cause bias Completely randomized design: experimental design; all the subjects are allocated at random among all the treatments

### 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

#### "I was shooting for a perfect 4.0 GPA this semester. Having StudySoup as a study aid was critical to helping me achieve my goal...and I nailed it!"

#### "When you're taking detailed notes and trying to help everyone else out in the class, it really helps you learn and understand the material...plus I made $280 on my first study guide!"

#### "I was shooting for a perfect 4.0 GPA this semester. Having StudySoup as a study aid was critical to helping me achieve my goal...and I nailed it!"

#### "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.