STA 210

Treatment

what is being "done" to the subjects. type of intervention.

Example: A study is performed to see if reading more produces a higher IQ (Identify the explanatory and response variable)

Explanatory: Reading. Response: IQ (Notice: when labeling the variables I did not assign quantities to them)

Sample

Part of the population we collect information from. - used to draw conclusion about the entire group.

Census

Sample survey that looks at the entire population. -problem with census: time consuming, money - expensive to do, supply.

(In response to what is Confounding?) What does this mean?

you have A and B, one of them is causing C, but you can't distinguish between the two. I.E. it may appear that A causes C, but really it is B that causes C.

Placebo Effect

real response to a fake treatment.

Explanatory Variable

variable that changes

Population

entire group we are interested in studying.

About.... die in traffic accidents

43,000

Correlation vs. Causation

-Correlation: straight-line association between two things -> it is visual. -Causation: When one thing causes the other.

Experiments

-Deliberately imposes some treatment to the individuals in your study. -The purpose of an experiment is to study whether the treatment causes a change in the response of the individuals. -Give good evidence for cause and effect.

Observational Studies

-Observes individuals and measures variables of interest. -Hands off. -The purpose of an observational study is to describe some group or situation. -no evidence - you can't use it as evidence.

About... of Americans identify themselves as Latinos

14%

About... deaths are from AIDS

16,000

About... deaths are from homicides

17,000

About... Americans die each year

2.4 million

U.S. Population is about...

300 million

Each year about... babies are born in the U.S.

4 million

There are about... black americans

40 Million

Statistical Significance

A difference between treatments that is sufficiently large that it is unlikely to have occurred by chance alone.

Explain how slippery evidence affects human inference?

At first glance we infer one thing, but with further review we see something else is true.

From the table, 11.4% of female corrective lens wearers wear contact lenses. The corresponding percentage for males is? a. 2.9%. b. 6.3%. c. 6.7%. d. None of the above.

B. 6.3

Roughly 1 in 4 who die, die from... more or less

Cancer

Explain the difference between Correlation and Causation.

Correlation is a straight line association between two things, it is visual. Causation is when one thing causes the other.

Dr. Stat's library of literary classics includes issues of certain not-so-scholarly periodicals. The total number of issues of four such periodicals, together with the percentages of Dr. Stat's entire library that they comprise, are as follows: One of the percentages in this table is incorrect. Which is it? a. 18.6% (Spider-Man) b. 6.2% (Soldier of Fortune) c. 3.1% (WWF) d. 7.0% (Mad)

D. 7.0 (MAD)

Roughly 1 in 4 who die, die from...

Heart Disease

How are lurking variables related to Simpson's Paradox?

Lurking variables can cause Simpson's Paradox to occur. We need to split the table up by the lurking variable.

What is the link between a lurking variable and confounding?

Lurking variables cause confounding.

Correlation

Mutual or complementary relationship - a relationship where two or more things are mutual or complementary.

Lurking Variable

NOT TAKEN IN ACCOUNT random variable not explanatory or response, but could influence them

Simpson's Paradox

Occurs when an association between two variables is reversed upon observing a third variable. -In probability and statistics, Simpson's paradox is a paradox in which a correlation present in different groups is reversed when the groups are combined. -An association in sub-populations may be reversed in the population. It appears that two sets of data separately support a certain hypothesis, but, when considered together, they support the opposite hypothesis.

Negative Association

Points have a downward trend from left to right.

Positive Association

Points have an upward trend from left to right.

Randomization

Produces groups of units that should be similar in all aspects (or eliminates potential biases due to order in which treatments are administered).

Confounding Variable

TAKEN IN ACCOUNT variable that you don't account for (example: age who measuring activity level and weight gain)

Objects

Usually are described by a set or data. They could be: people, animals, ice cream flavors, plants, fish, car.

Scatterplot

Visual way of assessing association, both direction and strength.

Correlation Coefficient

Way of summarizing the relationship you'd see between two variables that you could represent with a scatterplot.

Why is it important to critically read news articles, and why should we be careful about spreading information we are not confident about?

We need to critically read to make sure that what we are reading is true.

Subjects

What is being treated or studied.

Which of the following statements do you think could possibly be true? a. The number of students enrolled at Midville University decreased by 10.4% last year. b. A basketball team took 20 free throws in a game last week and made 72.6% of them. c. Yesterday it was 30° (Fahrenheit) in Chicago. Today it warmed up to 60°. This is a 50% increase in the temperature. d. My weight decreased by 10% last year but then increased by 10% in the first two months of this year. Thus, my overall weight from the beginning of last year until now is unchanged.

a. The number of students enrolled at Midville University decreased by 10.4% last year.

Categorical variables

anything you describe with quality. example: gender, make of a car, ice cream flavor.

Confounding often defeats attempts to show that one variable causes changes in another variable. Confounding means that a. the effects of two or more variables are mixed up, so we cannot say which is causing the response. b. we don't know which is the response variable and which is the explanatory variable. c. we would get widely varied results if we repeated the study many times. d. we have scheduled too many treatments for the number of subjects we have.

a. the effects of two or more variables are mixed up, so we cannot say which is causing the response

Variables

any characteristic of an individual. -a variable can take on different values for different individuals.

Numerical variable

anything you describe with numbers. example: speed, age, price, number of elevators in a building.

Which of these is an example of Simpson's paradox? a. Teachers' salaries and sales of alcoholic beverages have risen together over time, but paying teachers more does not cause higher alcohol sales. b. In the 1970s males were admitted to each of six departments at Berkeley at rates at essentially equal to or less than females, but when the data from all six departments were combined, males showed a much higher overall admission rate. c. The percent of surgery patients given Anesthetic A who die is higher than the percent for Anesthetic B, but this is because A is used in more serious surgeries. d. States in which a smaller percent of students take the SAT exam have higher median scores on the SAT.

b. In the 1970s males were admitted to each of six departments at Berkeley at rates at essentially equal to or less than females, but when the data from all six departments were combined, males showed a much higher overall admission rate.

A student reports that of a random sample of 200 college undergraduates, 37% were female and 41% were male. We know that the student has made a mistake because a. it is highly unlikely that a sample of 200 undergraduates would have as few as 37% females. b. we know from class that approximately 60% of all undergraduates are female c. 37% plus 41% is not 100%. d. 37% of 200 would mean there was a "partial female" in the sample.

c. 37% plus 41% is not 100%.

What is the effect of an outlier on the value of a correlation coefficient? a. An outlier will always decrease a correlation coefficient. b. An outlier will always increase a correlation coefficient. c. An outlier might either decrease or increase a correlation coefficient, depending on where it is in relation to the other points. e. An outlier will have no effect on a correlation coefficient.

c. An outlier might either decrease or increase a correlation coefficient, depending on where it is in relation to the other points.

Which of the following statements is true? a. Legitimate correlation never implies causation. b. Legitimate correlation does not necessarily imply causation. c. Legitimate correlation is equivalent to causation.

c. Legitimate correlation is equivalent to causation.

In the Nova Southeastern on-line learning example versus face-to-face discussed in class, the primary source of confounding was caused by a. massive, unexpected computer failures for the on-line group b. the placebo effect c. one group of subjects having better prepared backgrounds than the other d. the randomization of students to the two treatments

c. one group of subjects having better prepared backgrounds than the other

Suppose a correlation is found to be very weak. What does this mean about the relationship between the two variables? a. There is no linear relationship between the two variables being measured. b. There may be separate linear relationships that are being masked by a third variable that was not accounted for. c. There may be a different type of relationship between the variables; just not a linear one. d. All of the above.

d. All of the above.

Which one of these statistics is unaffected by outliers? a. Mean b. Interquartile range c. Standard deviation d. Median

d. Median

How well does the number of beers a student drinks predict his or her blood alcohol content? Sixteen student volunteers at The Ohio State University drank a randomly assigned number of cans of beer. Thirty minutes later, a police officer measured their blood alcohol content (BAC). A scatterplot of the data appears below. The scatterplot shows a. a weak negative relationship. b. a moderately strong negative relationship. c. almost no relationship. d. a moderately strong positive straight-line relationship between number of beers and BAC.

d. a moderately strong positive straight-line relationship between number of beers and BAC.

Response Variable

measured variable

percent of change formula

new-old / old