MCQs In Statistics …

04 May MCQs In Statistics …

The following   are properties of Chi Square Distributions (X2)
1. Values are nonnegative and include zero as a possibility
2. Positively Skewed Distribution
3. Consist of a Family of Distributions based on degrees of freedom
4. All of the above is   true
5. None of the above is true

 

 

 

The Chi Square   (X2) value for data consisting of 20 samples and an alpha value of   .025 is equivalent to
1. 34.17
2. 30.14
3. 32.85
4. 31.41

 

 

 

When computing   Chi Square (X2) we generally see that:
1. large values of Chi Square indicate agreement between the two sets of   frequencies
2. large values of Chi   Square indicate disagreement between the two sets of frequencies
3. Chi Sqare uses only continuous variables
4. Chi Square uses continuous and catagorical variables
5. None of the above

 

 

 

In a chi-square   test comparing observed to expected frequencies, we fail to reject the null   hypothesis whenever the observed frequencies are
1. each approximately   equal to their corresponding expected frequency
2. significantly greater than the expected frequencies
3. considerably smaller than the expected frequencies
4. not equal
5. none of the above

 

 

 

You have   calculated the chi-square test statistic in a test of independence for data   consisting of a sample size 20 and determined that the Chi Square critical   value is -4.23. Therefore, at the alpha .05 level, you will know that you
1. reject the null   hypothesis
2. Accept the null hypothesis
3. observed frequencies that were greater than the corresponding expected   frequencies
4. all of the above
5. none of the above

 

 

 

You have   developed a simulation model that predicts gas mileage for vehicles based on   a series of variables. You compare your predicted data to actual data for 70   samples. You conduct a test at the alpha .05 level and calculate a chi-square   value of 88.2. What is your conclusion of this test?
1. Reject the null hypothesis
2. Accept the null   hypothesis
3. Unable to reject or accept the null hypothesis
4. None of the above

 

 

 

What is your   conclusion for a chi-square test with calculated value of 17.34 and critical   chi square value of 2.54?
1. Reject the null   hypothesis
2. Accept the null hypothesis
3. Unable to reject or accept the null hypothesis
4. None of the above

 

 

 

Find X 2(27,   0.10)?
1. 33.19
2. 34.38
3. 35.56
4. 36.74
5. 37.91

 

 

 

Determine the   most appropriate technique for the defined test criteria

Column   preview

 

ANOVA

Testing the effectiveness of     a vaccine considering four different control groups

 

Two Way ANOVA

Determine if a sample data     resembles a normal distribution

 

Chi Square

Predict final statistics     grades based on number of student absences from class

 

Linear Regression

An experiment to determine which     of three different missiles systems with four different propellant burn     rates is preferred

 

 

 

The following   best describe objectives of two way ANOVA
1. It is a hypothesis test on the variances of three or more populations
2. Two factors serve to   provide variation in the response
3. All of the above
4. None of the above

 

 

 

The following   is the result of a two way ANOVA:

 

Source of     Variation

DF

Sum of     Squares

Mean Square

F

Fcritical

 

Treatments     (Brand)

4

53,231

13307.75

95.56732

3.26

 

Blocks

3

116,218

38739.25

278.1993

3.49

 

Error

12

1,671

139.25

 

Total

19

171,120

What   explanation best describes these results?

1. Both Variables Blocks   and Treatments have a significant effect on the outcome
2. Only Blocks contribute to the outcome
3. Only Treatments contribute to the outcome
4. None of the above

 

 

 

What is the   linear model used to explain relationship between two variables in a   population?

1. Y = alpha + B (x)
2. Y = Bo + B1 (X) +   error
3. Y = mx + B
4. All of the above
5. None of the above

 

 

 

The following   best describes Linear Regression Analysis
1. Tests the linear relationship between input and output variables
2. Attempts to develop a linear solution to predict outcome on the basis of   input
3. Considers the linear line of “best fit” by using the method of   least squared errors
4. All of the above is   true
5. None of the above is true

 

 

 

The following   are coefficients of correlation, r. The one that indicates a negative   relationship between the input variable x and the output variable y is:
1. -1.5
2. -.71
3. 0.0
4. 0.8
5. None of the above

 

 

 

If all of the   values of an independent variable x equal the same number, then regressing a   dependent variable y on x will result in a correlation coefficient , r of:
1. 0.88
2. -1.5
3. -1.0
4. 0.0

 

 

 

In a simple   linear regression problem , which of the following table values would be   appropriate for a 95% confidence interval for the mean of y for a given value   of x if the sample size is 10?
1. 1.86
2. 1.81
3. 2.31
4. 2.26

 

 

 

The vertical   spread of the data points about the regression line is measured by
1. Correlation Coefficient
2. Coefficient of Determination
3. Standard Error of the   Estimate
4. The Y intercept
5. The slope of the equation

 

 

 

A regression   analysis between sales (y in $1000) and advertising (x in $100) resulted in   the following least squares line: = 75 +5x. This implies that if advertising   is $800, then the predicted amount of sales (in dollars) is:
1. $79,000
2. $75,040
3. $115,000
4. S40,000
5. None of the Above

Choose the best possible definition for the given terms

Column preview

 

Coefficient of Determination

Generally exists when SSR is high   and SSE is low

 

Coefficient of Linear Correlation

The difference between the   observed value (Y) and the predicted value (Y) at a given X

 

Residual

The technique for producing   a linear equation on the basis of minimizing SSE

 

Method of Least Squares

Is the measure of the strength of   a linear relationship

 

Strong Linear Relationship

Is the fraction of the total   variability of the dependent variable that is explained by the variability of   the independent