Show:
About Ms. Kenney
Random
Experimental Design
Confidence Interval
Calculations
100

Ms. Kenney's favorite snack food...

What are chips?

100
The 3 percentages in a normal distribution that are 1 SD, 2 SD, and 3 SD away.

68-95-99

100

The group that receives no treatment.

Control group

100

The 3 things you have to check before calculating an interval

Random, 10% Condition, and Large Counts

100

The probability of flipping 4 coins and getting heads 4 times.

1/16

200

The number of dogs Ms. Kenney has owned in her life....

What is 2?

200

The definition of U in probability.

Union (or)

200

When neither the researchers or the subjects know who gets the treatment or placebo

Double Blind

200

The general formula for confidence interval

point estimate +/- margin of error

200

The z* value when you do a 95% confidence interval.

1.96

300
Ms. Kenney's favorite condiment...

What is ketchup?

300

The definition of mutually exclusive.

Events A and B have no outcomes in common (cannot happen at the same time)

300

A sample that is made up of people who are easy to reach.

Convenience Sample

300

What the 10% condition checks for

Independence

300

The point estimate used to create the interval (0.088, 0.126).

0.107

400

The name of the part of Boston Ms. Kenney lived in after college....

What is Brighton?

400

Equation for residuals

Observed - predicted

400

When we create a list of every member of the population. From the list, we randomly select the first sample element from the first k subjects on the population list.

Systematic Random Sampling

400

What Large Counts checks for.

Normality

400

The t* value for a sample size of n = 85 and 98% intervals (from data table).

2.374

500
Ms. Kenney was dean once before being junior dean for a maternity for leave several years ago.  Who did she cover for while that person was on maternity leave?
Who is Ms. Bleakney
500

The 3 different ways to increase power.

Increase sample size, increase alpha level, and increase difference from null

500

A design that is used when the experiment has only two treatment conditions; and subjects can be grouped into pairs, based on some blocking variable. Then, within each pair, subjects are randomly assigned to different treatments. In some cases you give two treatments to the same experimental unit.

Matched Pairs Design

500

The 3 different ways you can check for normal/large samples for means.

Poplation is approx. normal, n >= 30, or dot plot shows no skewness/outliers

500

The interval for a 1-sample t-interval for µ where x̅ = 36, s = 2, n = 25, and CL = 95% (use a calculator)

(35.174, 36.826)