BUAL 2650 Final Exam |44 Questions
and Answers
alpha (a) - --level of significance
-allowance for a Type I error
-evidence is found significant we reject the null
- Hypothesis testing of proportions uses what tables? - -z tables
- p-value - --the probability of the null being true
-observed level of significance
1-test statistic from table
- p-value < a - -reject the null
- test statistic > CV - -we reject the null
- Two-Tailed p-value - -the p-value of a two tailed test is both shaded areas
added together
- An example in the judicial system - -the null is that the defendant is "not
guilty"
- Type I Error - --made in rejecting a null when it is actually true
-false positive
- Type II Error - --failing to reject the null when in fact it is false
-false negative
- Power of a Test - --the ability of the test to make the correct decision of
rejecting the null when it is false
-making the right decision
-1-P(Type II) aka 1-Beta
-want it to be high
-as a increases, B decreases
- Central Limit Theorem - -sampling distribution of any mean becomes
normal as the sample size grows
- Hypothesis testing of means uses what tables? - --t tables
-as degrees of freedom increase, t-models look more normal
, - How do we reduce both Type I & II errors? - -we increase the sample size
- Paired Data - -occurs as "before and after"
- What are the 3 properties of sample distribution and sample mean? - --the
mean of the sample means is equal to the population mean
-the standard deviation of the sample means is equal to the population
standard deviation, divided by the square root of n
-the standard error of the mean is the standard deviation of the sample
means
- Goodness of fit test - --determines if observed proportions conform to
expected ones
-levels of classification (k)
-df = k - 1
- Chi Square (x^2) - --the distribution used to test goodness of fit
-difference between observed and expected count, squared
-x^2 > CV, we reject null
-is right skewed and becomes broader when the df is increased
- Two-Tailed proportions - --a/2 = level of significance
-CI = 1-a
-must find 2 z scores on each end of the bell
- Dependent Variable - --Y
-variable we wish to predict
- Independent Variable - --X
-variables that have an impact on Y
- What are the Linear Regression Model Assumptions - -1. mean of the error
is always 0 for each level of the independent variable
2. the functional relationship b/w x & is linear
3. the error components are independent of each other
4. the variance of the error component is constant for all levels of the
independent variable
5. the probability distribution of the error is normal for each level of the
independent variable
- Least Squares Method - --finds the estimated coefficient model b0 and b1
in the simple linear regression model
-SSE, SST
- Coefficient of Determination - --R^2
and Answers
alpha (a) - --level of significance
-allowance for a Type I error
-evidence is found significant we reject the null
- Hypothesis testing of proportions uses what tables? - -z tables
- p-value - --the probability of the null being true
-observed level of significance
1-test statistic from table
- p-value < a - -reject the null
- test statistic > CV - -we reject the null
- Two-Tailed p-value - -the p-value of a two tailed test is both shaded areas
added together
- An example in the judicial system - -the null is that the defendant is "not
guilty"
- Type I Error - --made in rejecting a null when it is actually true
-false positive
- Type II Error - --failing to reject the null when in fact it is false
-false negative
- Power of a Test - --the ability of the test to make the correct decision of
rejecting the null when it is false
-making the right decision
-1-P(Type II) aka 1-Beta
-want it to be high
-as a increases, B decreases
- Central Limit Theorem - -sampling distribution of any mean becomes
normal as the sample size grows
- Hypothesis testing of means uses what tables? - --t tables
-as degrees of freedom increase, t-models look more normal
, - How do we reduce both Type I & II errors? - -we increase the sample size
- Paired Data - -occurs as "before and after"
- What are the 3 properties of sample distribution and sample mean? - --the
mean of the sample means is equal to the population mean
-the standard deviation of the sample means is equal to the population
standard deviation, divided by the square root of n
-the standard error of the mean is the standard deviation of the sample
means
- Goodness of fit test - --determines if observed proportions conform to
expected ones
-levels of classification (k)
-df = k - 1
- Chi Square (x^2) - --the distribution used to test goodness of fit
-difference between observed and expected count, squared
-x^2 > CV, we reject null
-is right skewed and becomes broader when the df is increased
- Two-Tailed proportions - --a/2 = level of significance
-CI = 1-a
-must find 2 z scores on each end of the bell
- Dependent Variable - --Y
-variable we wish to predict
- Independent Variable - --X
-variables that have an impact on Y
- What are the Linear Regression Model Assumptions - -1. mean of the error
is always 0 for each level of the independent variable
2. the functional relationship b/w x & is linear
3. the error components are independent of each other
4. the variance of the error component is constant for all levels of the
independent variable
5. the probability distribution of the error is normal for each level of the
independent variable
- Least Squares Method - --finds the estimated coefficient model b0 and b1
in the simple linear regression model
-SSE, SST
- Coefficient of Determination - --R^2
