If a hypothesis test leads to the rejection of the null hypothesis, a _____.

Two approaches to drawing a conclusion in a hypothesis test are _____.

Which of the following hypotheses is not a valid null hypothesis?

When the rejection region is in the lower tail of the sampling distribution, the p-value is the area under the curve _____.

Excel's __________ function can be used to calculate a p-value for a hypothesis test.

In the hypothesis testing procedure, α is _____.

In a two-tailed hypothesis test, the null hypothesis should be rejected if the p-value is _____.

Which of the following is an improper form of the null and alternative hypotheses?

When each data value in one sample is matched with a corresponding data value in another sample, the samples are known as _____.

The standard error of x̄₁ - x̄₂ is the _____.

A company wants to identify which of two production methods has the smaller completion time. One sample of workers is randomly selected and each worker first uses one method and then uses the other method. The sampling procedure being used to collect completion time data is based on _____ samples.

Independent simple random samples are selected to test the difference between the means of two populations whose variances are not known. The sample sizes are n₁ = 32 and n₂ = 40. The correct distribution to use is the _____ distribution.

In a simple regression analysis (where y is a dependent and x an independent variable), if the slope is positive, then it must be true that _____.

Which of the following is correct?

The proportion of the variation in the dependent variable y that is explained by the estimated regression equation is measured by the _____.

The least squares criterion is _____.

A news reporter states that the average number of temperature in January has never dropped below 10 degrees Fahrenheit. You go online to research this claim. The appropriate hypotheses are:

The p-value

A local bakery generates $2,500 of revenue per day, on average. The bakery's owner plans to increase expenses on the advertisement, hoping that this results in higher revenue. What is a Type I error for this situation?

The normal probability distribution can be used to approximate the sampling distribution of as long as:

A student wants to determine if pennies are really fair, meaning equally likely to land heads up or tails up. He flips a random sample of 50 pennies and finds that 28 of them land heads up. What are the appropriate null and alternative hypotheses?

Peter spends about 18 minutes repairing one smartphone, on average. After purchasing new equipment, he expects to perform some operations quicker, reducing the average repair time. What is a Type II error for this situation?

For a lower tail test, the p-value is the probability of obtaining a value for the test statistic:

The average hourly wage of computer programmers with 2 years of experience has been $21.80. Because of high demand for computer programmers, it is believed there has been a significant increase in the average wage of computer programmers. To test whether there has been an increase, the correct hypotheses to be tested are:

The average gasoline price of one of the major oil companies has been hovering around $2.20 per gallon. Because of cost reduction measures, it is announced that there will be a significant reduction in the average price over the next month. To test this, we wait one month, then randomly select a sample of 36 of the company's gas stations. We find that the average price for the stations in the sample was $2.15. The standard deviation of the prices for the selected gas stations is $.10. State the appropriate null and alternative hypotheses for testing the company's claim.

In hypothesis testing, if the null hypothesis has not been rejected when the null hypothesis has been true,

The average number of hours for a random sample of mail order pharmacists from company A was 50.1 hours last year. It is believed that changes to medical insurance have led to a reduction in the average work week. To test the validity of this belief, the hypotheses are:

For the case where σ is unknown, the test statistic has a t distribution. How many degrees of freedom does it have?

As the test statistic becomes larger, the p-value:

Applications of hypothesis testing that only control for the Type I error are called:

The p-value is a probability that measures the evidence against the:

Whenever the probability of making a Type II error has not been determined and controlled, only two conclusions are possible. We either reject H₀ or:

Which of the following null hypotheses cannot be correct?

What are the most common choices for the level of significance?

For the case where σ is unknown, which statistic is used to estimate σ?

the hypothesis tentatively assumed true in the hypothesis testing procedure

A hypothesis test in which rejection of the null hypothesis occurs for values of the test statistic in either tail of its sampling distribution

The hypothesis concluded to be true if the null hypothesis is rejected. Known as the research hypothesis

A probability that provides a measure of the evidence against the null hypothesis provided by the sample/probability used to test the null hypothesis. Smaller _______ indicate more evidence against H₀.

For a ______ test, the p-value is the probability of obtaining a value for the test statistic as small as or smaller than that provided by the sample

For an______ test, the p-value is the probability of obtaining a value for the test statistic as large as or larger than that provided by the sample

For a _______ test, the p-value is the probability of obtaining a value for the test statistic at least as unlikely as or more unlikely than that provided by the sample

The error/probability of rejecting H₀ when it is true

The probability of making a Type 1 error when the null hypothesis is true as an equality

The error/probability of accepting/not rejecting H₀ when it is false/should have been rejected

A value that is compared with the test statistic to determine whether H₀ should be rejected

A hypothesis test in which rejection of the null hypothesis occurs for values of the test statistic in one tail of its sampling distribution

Samples selected from two populations in such a way that the elements making up one sample are chosen independently of the elements making up the other sample

Samples in which each data value of one sample is matched with a corresponding data value of the other sample

An estimator of a population proportion obtained by computing a weighted average of the point estimators obtained from two independent samples

The variable that is doing the predicting or explaining. It is denoted by x

The variable that is being predicted or explained. It is denoted by y

The difference between the observed value of the dependent variable (y_i) and the value predicted variable of the dependent variable (y-hat i) using the estimated regression equation; for the ith observation the ith residual is (y_i -y-hat i). Represents the error in using (y-hat i) to estimate y_i. The value of ___ is a measure of the error in using the estimated regression equation to predict the values of the dependent variable in the sample. Can think of a measure of how well the observations cluster about the (y-hat) line. Can be thought of as the unexpected portion of SST

The difference (yi- ybar) provides a measure of the error involved in using (ybar) to predict sales. The corresponding sum of squares, called the total sum of squares. Can think of ___ as a measure of how well the observations cluster about the ybar line

To measure how much the (yhat) values on the estimated regression line deviate from (yhat), another sum of squares is computed. This sum of squares, called the sum of squares due to regression. Can be thought of as the explained portion of SST

The unbiased estimate of the variance of the error term $$\sigma^2$$  . It is called mean square error or s²

The sum of squares due to regression (SSR), divided by its degrees of freedom provides another independent estimate of $$\sigma^2$$  . This estimate in called the mean square sue to regression, or simple mean square regression

Regression analysis involving one independent variable and one dependent variable in which the relationship between the variable is approximated by a straight line

The equation that describes how y is related to x and an error term

The estimate of the regression equation developed from sample data by using the least squares method

The equation that describes how the mean or expected value of the dependent variable is related to the independent variable

A graph of bivariate data in which the independent variable is on the horizontal axis and the dependent variable is on the vertical axis

A measure of the goodness of fit of the estimated regression equation. It can be interpreted as the proportion of the variability in the dependent variable y that is explained by the estimated regression equation

The square root of the mean square error, denoted by x. It is the estimate of 𝜎, the standard deviation of the error term

The interval estimate of the mean value of y for a given value of x

The interval estimate of an individual value of y for a given value of x

Graphical representation of the residuals used to determine whether the assumptions made about the regression model appear to be valid

– the analysis of the residuals used to determine whether the assumptions made about the regression model appear to be valid. Residual analysis is also used to identify outliers and influential observations

The value obtained by dividing a residual by its standard deviation

A data point or observation that does not fit the trend shown by the remaining data

An observation that has a strong influence or effect on the regression results

Which of the following scenarios follows a matched sample design?

When completing a two-tailed hypothesis test about the difference between two population means, the

Regarding hypothesis tests about p1 - p2 , the pooled estimate of P is a:

To construct an interval estimate for the difference between the means of two populations with sample sizes of n₁ and n₂ when the two population standard deviations are known, what are the degrees of freedom to compute the z value needed for the interval estimate?

Regarding inferences about the difference between two population means, the alternative to the matched sample design is:

A researcher recruits 25 people to participate in a study on alcohol consumption and its interactions with Tylenol. The 25 participants had to come to a check-in center every day at 7:00 a.m. for one week. They were given various amounts of alcohol. Each day, each participant would flip a coin to determine if they also took Tylenol with their alcohol. They found that their BAC was 25% higher on days when they were given Tylenol with their alcohol than when they drank alcohol alone. This is an example of a(n):

If we are interested in testing whether the proportion of items in population 1 is larger than the proportion of items in population 2, then the:

The matched sample design often leads to a smaller sampling error than the independent sample design. The primary reason is that in a matched sample design:

Suppose we have a t distribution based upon two sample means with unknown population standard deviations, which we are unwilling to assume are equal. When we calculate the appropriate degrees of freedom, we should:

A professor of statistics wants to identify whether exam scores at the end of the second semester compared to the first semester improve among her 40 students. The sampling procedure being used to collect data is based on

In regression analysis, the equation in the form y = 𝛽₀ + 𝛽₁x + ε is called the:

The tests of significance in regression analysis are based on assumptions about the error term ɛ . One such assumption is that the variance of ɛ, denoted by 𝝈², is:

The tests of significance in regression analysis are based on assumptions about the error term ɛ. One such assumption is that the error term follows ɛ a(n) _____ distribution for all values of x.

If a significant relationship exists between x and y and the coefficient of determination shows that the fit is good, the estimated regression equation should be useful for:

The value of the coefficient of correlation (r):

In regression analysis, the variable that is being predicted is the:

Graphical representation of the residuals that can be used to determine whether the assumptions made about the regression model appear to be valid is called a:

Observations with extreme values for the independent variables are called:

When working with regression analysis, an outlier is:

The model developed from sample data that has the form is known as the:

If a residual plot of x versus the residuals, y - ŷ, shows a non-linear pattern, then we should conclude that:

The mathematical equation relating the independent variable to the expected value of the dependent variable, , is known as the:

The coefficient of determination:

Suppose a residual plot of x verses the residuals, y - ŷ, shows a nonconstant variance. In particular, as the values of x increase, suppose that the values of the residuals also increase. This means that:

If you suspect that you have an influential observation, the first thing you should do is:

Larger values of r² imply that the observations are more closely grouped about the:

The tests of significance in regression analysis are based on several assumptions about the error term ɛ. Additionally, we make an assumption about the form of the relationship between x and y. We assume that the relationship between x and y is:

The tests of significance in regression analysis are based on assumptions about the error term ɛ. One such assumption is that the values of ɛ are:

The difference between the observed value of the dependent variable and the value predicted using the estimated regression equation is called a(n):

The tests of significance in regression analysis are based on assumptions about the error term ɛ. One such assumption is that the error term ɛ is a random variable with a mean or expected value of:

When studying the relationship between two quantitative variables, whenever we want to predict an individual value of y for a new observation corresponding to a given value of x, we should use a(n):

When studying the relationship between two quantitative variables, an interval estimate of the mean value of y for a given value of x is called a(n):

When constructing a confidence or a prediction interval to quantify the relationship between two quantitative variables, what distribution do confidence and prediction intervals follow?

A ________ is a graph of the standardized residuals plotted against values of the normal scores. This helps to determine whether the assumption that the error term has a normal probability distribution appears to be valid.

Which of the following statements is false?

An observation that has a strong influence or effect on the regression results is called a(n):

If the coefficient of determination is a positive value, then the coefficient of correlation:

When constructing a confidence or a prediction interval to quantify the relationship between two quantitative variables, the appropriate degrees of freedom are:

An F test, based on the F probability distribution, can be used to test for:

In a simple linear regression model, the error term ε accounts for the variability in ______ that cannot be explained by the linear relationship between x and y.