Statistics Quiz on Hypothesis Testing

<p>Chi-square test (D)</p> Signup and view all the answers

<p>Examining means before and after an intervention (A)</p> Signup and view all the answers

<p>Related sample T-test (C)</p> Signup and view all the answers

<p>Continuous variables (D)</p> Signup and view all the answers

<p>Comparing means from two different groups (C)</p> Signup and view all the answers

<p>One-tail and two-tail (B)</p> Signup and view all the answers

<p>The level of significance (B)</p> Signup and view all the answers

<p>3.182 (D)</p> Signup and view all the answers

<p>9.925 (B)</p> Signup and view all the answers

<p>0.001 (A)</p> Signup and view all the answers

<p>Differences in only one direction (A)</p> Signup and view all the answers

<p>3.747 (D)</p> Signup and view all the answers

<p>4 (C)</p> Signup and view all the answers

<p>2.776 (A), 2.776 (C)</p> Signup and view all the answers

<p>ρ = 0 (B)</p> Signup and view all the answers

<p>2.976 (D)</p> Signup and view all the answers

<p>t = r / sqrt(1 - r^2) (A)</p> Signup and view all the answers

<p>1 - α/2 (B)</p> Signup and view all the answers

<p>4.39 (A)</p> Signup and view all the answers

<p>t &gt; tcritic (A)</p> Signup and view all the answers

<p>4 (C)</p> Signup and view all the answers

<p>To establish a hypothesis about customer satisfaction (A)</p> Signup and view all the answers

<p>The dependent variable (D)</p> Signup and view all the answers

<p>Location and service (D)</p> Signup and view all the answers

<p>The effect of independent variables on the dependent variable (D)</p> Signup and view all the answers

<p>Ho: Some variables affect satisfaction (B)</p> Signup and view all the answers

<p>Error term (ε) (D)</p> Signup and view all the answers

<p>Multiple linear regression (B)</p> Signup and view all the answers

<p>Intercept of the regression line (A)</p> Signup and view all the answers

<p>Both A and C (B)</p> Signup and view all the answers

<p>The strength and direction of an association between two variables (B)</p> Signup and view all the answers

<p>Uncontrolled factors that can affect the relationship between IV and DV (A)</p> Signup and view all the answers

<p>Underlying knowledge and theories about the variables (C)</p> Signup and view all the answers

<p>Investigating the nature and degree of association between variables (C)</p> Signup and view all the answers

<p>Account for and eliminate potential confounders (B)</p> Signup and view all the answers

<p>The sample size must exceed a certain threshold (B)</p> Signup and view all the answers

<p>Nature and degree of association (B)</p> Signup and view all the answers

<p>Coding the category of interest as 1 and others as 0 (C)</p> Signup and view all the answers

<p>n-1 categories (C)</p> Signup and view all the answers

<p>Female, coded as 0 (C)</p> Signup and view all the answers

<p>Light user (B)</p> Signup and view all the answers

<p>In both binary and multinomial cases (C)</p> Signup and view all the answers

<p>To analyze the relationship between two variables (D)</p> Signup and view all the answers

<p>The effect of customer satisfaction on likelihood to recommend (C)</p> Signup and view all the answers

<p>Coded as 0 in all dummies (A)</p> Signup and view all the answers

A test used to analyze data on a single characteristic.

A test used to analyze data on two characteristics.

Analyzing whether the average values for a group differ between multiple groups.

A statistical test to compare the mean of a single group to a known value or to compare means of two groups.

A test used to compare means from two related groups.

A test to compare two unrelated groups' characteristics.

A statistical method used to find a relationship between two continuous variables.

A test to analyze the association between categorical variables.

A statistical test used to determine if there's a significant difference between the means of two groups when the population standard deviation isn't known.

A hypothesis test where the critical region is one-sided, looking for a difference in only one direction.

A hypothesis test where the critical region is two-sided, looking for differences in both directions.

The probability of rejecting a true null hypothesis (alpha).

A parameter in the student's t distribution that describes the shape of the distribution and depends on the sample size.

The value that separates the rejection region from the acceptance region in a hypothesis test.

A level of confidence of 95%.

A probability distribution used to estimate population parameters when the population standard deviation is unknown.

Correlation analysis is used to determine if there is a relationship between two continuous variables. It measures the strength and direction of that relationship.

The null hypothesis states that there is no relationship between the two variables being studied. It assumes that any observed correlation is due to chance.

The alternative hypothesis states that there is a relationship between the two variables. It contradicts the null hypothesis.

The test statistic (t) for correlation analysis is calculated using the formula: t = r / sqrt((1-r^2)/(n-2)), where r is the correlation coefficient and n is the sample size.

The critical t-value is the value that separates the rejection region from the non-rejection region of the t-distribution for a given alpha level.

If the calculated t-value is greater than the critical t-value, you reject the null hypothesis. If the calculated t-value is less than the critical t-value, you fail to reject the null hypothesis.

Rejecting the null hypothesis means there is enough evidence to suggest that there is a statistically significant relationship between the two variables.

Failing to reject the null hypothesis means there is not enough evidence to conclude that there is a statistically significant relationship between the two variables.

A statistical measure that describes the strength and direction of the relationship between two variables.

A cause-and-effect relationship between two variables, where one variable directly influences the other.

A statistical technique used to predict the value of a dependent variable based on the value of one or more independent variables.

The variable that is manipulated or changed in an experiment to see its effect on the dependent variable.

The variable that is being measured or observed in an experiment, and its value is influenced by the independent variable.

An extraneous variable that can influence both the independent and dependent variables, making it difficult to determine the true relationship between them.

The independent variable must occur before the dependent variable in order for a causal relationship to exist.

A relationship between two variables, where the change in one variable is related to the change in the other, but not necessarily a causal relationship.

A mathematical formula used to predict the value of a dependent variable (Y) based on the values of one or more independent variables (X).

A type of regression analysis that uses only one independent variable to predict the dependent variable.

A type of regression analysis that uses more than one independent variable to predict the dependent variable.

The value of the dependent variable when all independent variables are equal to zero.

The numerical values that represent the relationship between each independent variable and the dependent variable.

The difference between the actual value of the dependent variable and the predicted value based on the regression equation.

A method used to represent categorical variables in regression analysis by converting them into numerical variables. Each category is assigned a '1' if it's the category of interest and '0' for all other categories. For 'n' categories, we code 'n-1' dummy variables.

In dummy variable coding, the category that isn't explicitly coded with a '1' is considered the baseline. It's the reference point for comparison.

A statistical method used to analyze the relationship between two variables: a dependent variable and a single independent variable. It aims to find a linear equation that best predicts the dependent variable based on the independent variable.

In regression analysis, a hypothesis test is used to determine if there's a statistically significant relationship between the independent and dependent variables. It involves setting up a null hypothesis (no relationship) and an alternative hypothesis (there's a relationship).

This is a typical scenario where simple regression is used. We want to see if customer satisfaction (independent variable) has a significant impact on their likelihood to recommend (dependent variable).

In regression, the variable we are trying to predict. It's the outcome or result that is influenced by the independent variable.

The variable that influences or predicts the dependent variable. It's the controlled variable.

The equation that represents the relationship between the independent variable (x) and the dependent variable (y). It's in the form y = mx + b, where 'm' is the slope (strength of the relationship) and 'b' is the y-intercept (value of y when x is zero).