stats final new material – Flashcards
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For which of the following Pearson correlations would the data points be clustered most closely around a straight line?
r = -0.10
There is no relationship between the correlation and how close the points are to a straight line.
r = -0.70
r = +0.40
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r = -0.70
closest to one, the strongest corrilation
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A college professor reports that students who finish exams early tend to get better grades than students who hold on to exams until the last possible moment. The correlation between exam score and amount of time spent on the exam is an example of
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a negative correlation
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Suppose the correlation between height and weight for adults is +0.40. What proportion (or percent) of the variability in weight can be explained by the relationship with height?
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16%
r2 is the variability, .40 is r to the square root of r is .16
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For a group of graduating college seniors, a researcher records each student's rank in his/her high school graduating class and the student's rank in the college graduating class. Which correlation should be used to measure the relationship between these two variables?
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Spearman correlation
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A researcher classifies participants by gender (assuming only two genders) and by whether or not they routinely watch the evening news on television. For the resulting data, what is the correct method for measuring the relationship between the two variables?
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phi-coefficient
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For the linear equation Y = 2X + 4, if X increases by 1 point, how much will Y increase?
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2 points
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A linear regression equation has b = 3 and a = - 6. What is the predicted value of Y for X = 4?
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6
y=bx+a
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A multiple regression equation with two predictor variables produces R2 = 0.10. What portion of the variability for the Y scores is predicted by the equation?
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10%
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The Pearson correlation between X1 and Y is r = 0.40 and SSY = 100. When a second variable, X2, is added to the regression equation, we obtain R2 = 0.25. How much additional variance is predicted by adding the second variable compared to using X1 alone?
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9 points
25-16
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The Pearson correlation between X1 and Y is r = 0.50 and the correlation between X2 and Y is r = 0.20. How much variance for the Y scores is predicted by using both X1 and X2 as predictor variables in a multiple regression equation?
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can not be determined
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What is referred to by the term observed frequencies?
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the frequencies found in the sample data
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What conclusion is appropriate if a chi-square test produces a chi-square statistic near zero?
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There is a good fit between the sample data and the null hypothesis.
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Which of the following accurately describes the chi-square test for goodness of fit?
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It is similar to a single-sample t test because it uses one sample to test a hypothesis about one population.
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Under what circumstances will the chi-square test for goodness of fit produce a large value for chi-square?
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when the sample proportions are much different than the hypothesized population proportions
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What is stated by the null hypothesis for the chi-square test for independence?
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There is no relationship between the two variables.
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Direction of relationship
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positive correlation-two variables change in the same direction
negative correlation- two variables change in opposite directions
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Form of relationship
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linear correlations, closer to a straight line the stronger the correlation
measured by Pearson correlation
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Strength or consistency of relationship
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how close it is to a straight line
0= no relationship
1=perfect relationship
effect size
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Pearson corrilation
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interval/ratio
numeric
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Spearman corrilation
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-two ordinal variables (freshman, junior)
- ranked
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Point-Biserial
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-interval or ratio
-one dichotomous variable
-one non dichotomous variable
like independent samples t-test
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Phi Coefficient
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-two dichotomous variables
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regression equation
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y=bx+a
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observed frequencies
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number of individuals form the sample who are classified in a particular category
frequencies in the sample data
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expected frequency
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frequencies predicted by the null
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X2 equation
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(f0 * Fe)
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fe
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Chi-squared test for goodness of fit
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no means used
how well obtained sample proportions fit the population proportions specified by the null
uses sample data to test hypotheses about the shape or proportions of a population distribution
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Chi-squared test for independance
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no means used
relationship between two variables in the population
.Testing hypotheses about differences between
proportions for two or more populations.
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Null hypothesis for chi squared test for goodness of fit
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specifies the proportion of
the population that should be in each category
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null hypothesis Chi-squared test for independance
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1. that there is no relationship between the two variables; that is, the two variables are independent
2. that the proportions (the distribution across categories) are the same for all of the populations
