Stat 250 Review
Final Exam Review Part VI
Definitions and Terms: Know the major definitions and terms for example
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
Population
Sample
Descriptive Statistic
Inferential Statistics
Parameter vs Statistics
Variable
a. Categorical
Statistic
estimates Parameter
b. Quantitative estimates , sample mean
, population
i. Discrete mean s, sample standard estimates
, population ii. Continuous deviation standard deviation
Random Variable estimates P, population
ˆ
p , sample
Sampling Distributions proportion proportion
Parameter (Defines a population)
Statistic (calculated from sample to estimate a parameter)
Central Limit Theorem
Law of Large Numbers
Confidence Level
(1- )*100
Type I error (rejecting the null hypothesis when in fact it is true)
Type II error (not rejecting the null hypothesis when in fact the null is not true)
What is true
What you did
Do not
Reject H0
Reject H0
H0 true
No Error
Type I error Ha True
Type II
Error
No Error
15. Level of Significance (The probability of making a Type I error)
16. Interpretation of a confidence interval
17. P-value
a. The probability of making a type I error based on your sample
b. The probability, computed supposing the H0 to be true, that the test statistic will take a value at least as extreme as that actually observed.
18. Interpretation of a test of significance (hypothesis test)
Types of Problems
1. Reading and interpreting graphs (make sure you read the labels so you know units and whether the graph is frequency (counts) or relative frequency (percents, ratios, probabilities). 2. Calculating Measures of Center
a. Mean
b. Median
3. Calculating Measures of Spread
a. Range
b. You will not have to calculate the standard deviation, but you must understand what the standard deviation is – the average distance each data value is from the mean. c. Interquartile Range (Q3 – Q1)
i. 1st quartile ii. 2nd
Definitions and Terms: Know the major definitions and terms for example
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
Population
Sample
Descriptive Statistic
Inferential Statistics
Parameter vs Statistics
Variable
a. Categorical
Statistic
estimates Parameter
b. Quantitative estimates , sample mean
, population
i. Discrete mean s, sample standard estimates
, population ii. Continuous deviation standard deviation
Random Variable estimates P, population
ˆ
p , sample
Sampling Distributions proportion proportion
Parameter (Defines a population)
Statistic (calculated from sample to estimate a parameter)
Central Limit Theorem
Law of Large Numbers
Confidence Level
(1- )*100
Type I error (rejecting the null hypothesis when in fact it is true)
Type II error (not rejecting the null hypothesis when in fact the null is not true)
What is true
What you did
Do not
Reject H0
Reject H0
H0 true
No Error
Type I error Ha True
Type II
Error
No Error
15. Level of Significance (The probability of making a Type I error)
16. Interpretation of a confidence interval
17. P-value
a. The probability of making a type I error based on your sample
b. The probability, computed supposing the H0 to be true, that the test statistic will take a value at least as extreme as that actually observed.
18. Interpretation of a test of significance (hypothesis test)
Types of Problems
1. Reading and interpreting graphs (make sure you read the labels so you know units and whether the graph is frequency (counts) or relative frequency (percents, ratios, probabilities). 2. Calculating Measures of Center
a. Mean
b. Median
3. Calculating Measures of Spread
a. Range
b. You will not have to calculate the standard deviation, but you must understand what the standard deviation is – the average distance each data value is from the mean. c. Interquartile Range (Q3 – Q1)
i. 1st quartile ii. 2nd