highlight the correct answer In Testing Hypothesis, the determination of one tai
ID: 3225373 • Letter: H
Question
highlight the correct answer
In Testing Hypothesis, the determination of one tail versus two tail test is based on:
The sign of the null hypothesis
The claim
The critical values
The sign of the alternative hypothesis
The major difference between Student’s t distribution and Standard Normal Distribution is:
t is not symmetric
Z requires larger degrees of freedom
The mean of t is not zero
The standard deviation of t is not 1
In Testing Hypothesis, we always test:
The claim
The null hypothesis
The alternative hypothesis
The level of significance
In testing hypothesis about the means of two populations, the choice of our test statistics depends on:
If populations standard deviations are known or unknown
If the samples are large or small
If the level of significance is 0.10 or higher
If the standard deviations of the two samples are known
Chi-Square two tail critical values for a certain level of significance are not the same because:
The distribution is symmetric
They are on both sides of the mean
The distribution is not symmetric
The degrees of freedom are not the same
ANOVA process could be used to examine:
One effect
Two effects
Interaction
All of the above
R-square is used to explain :
If the relation between X & Y is linear
If X &Y are independent
If X depends on Y
If the variation of Y is due to changes in X
Simple Linear Regression is represented by:
A quadratic equation
An equation of line
An exponential equation
A logarithmic equation
Simulating practical situations with mathematical equations is:
Statistical Experiment
Mathematical Model Building
Analysis process
Correlation Matrix
Least Square Estimators method, is used to:
Reduce Correlation
Test Hypothesis
Minimize Estimation Error
Analyze cause-effect situation
Correlation Matrix can be used to:
Substitute Regression
Perform ANOVA
Select Explanatory Variables
Improve Sample Data
A Regression Equation Can be:
Quadratic
Linear
Power
All of the above
A Multiple Linear Regression can be:
Logarithmic
Exponential
Linear
All of the above
A goodness of fit test can be used only to test:
A fit of normal distribution to the data
A fit of Binomial distribution to the data
A fit of continuous distribution to the data
All of the above
Test statistic used for test if independence of two variables is:
F
Chi-Square
Z
t
Explanation / Answer
Answers to all questions below:
In Testing Hypothesis, the determination of one tail versus two tail test is based on:
The sign of the null hypothesis
The claim
The critical values
The sign of the alternative hypothesis
The major difference between Student’s t distribution and Standard Normal Distribution is:
t is not symmetric
Z requires larger degrees of freedom
The mean of t is not zero
The standard deviation of t is not 1
In Testing Hypothesis, we always test:
The claim
The null hypothesis
The alternative hypothesis
The level of significance
In testing hypothesis about the means of two populations, the choice of our test statistics depends on:
If populations standard deviations are known or unknown
If the samples are large or small
If the level of significance is 0.10 or higher
If the standard deviations of the two samples are known
Chi-Square two tail critical values for a certain level of significance are not the same because:
The distribution is symmetric
They are on both sides of the mean
The distribution is not symmetric
The degrees of freedom are not the same
ANOVA process could be used to examine:
One effect
Two effects
Interaction
All of the above
R-square is used to explain :
If the relation between X & Y is linear
If X &Y are independent
If X depends on Y
If the variation of Y is due to changes in X
Simple Linear Regression is represented by:
A quadratic equation
An equation of line
An exponential equation
A logarithmic equation
Simulating practical situations with mathematical equations is:
Statistical Experiment
Mathematical Model Building
Analysis process
Correlation Matrix
Least Square Estimators method, is used to:
Reduce Correlation
Test Hypothesis
Minimize Estimation Error
Analyze cause-effect situation
Correlation Matrix can be used to:
Substitute Regression
Perform ANOVA
Select Explanatory Variables
Improve Sample Data
A Regression Equation Can be:
Quadratic
Linear
Power
All of the above
A Multiple Linear Regression can be:
Logarithmic
Exponential
Linear
All of the above
A goodness of fit test can be used only to test:
A fit of normal distribution to the data
A fit of Binomial distribution to the data
A fit of continuous distribution to the data
All of the above
Test statistic used for test if independence of two variables is:
F
Chi-Square
Z
t
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