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Stats 270 Final study guide questions and answers
Inferential statistics are usually based on data obtained from____ to make generalizations - Samples Which of the following is a discrete variable - Number of children Which of the following is a continuous variable - Time spent watching TV A nominal-level variable like marital status or gender is always - Discrete Categories of nominal level variables should be - Exhaustive, mutually exclusive to avoid ambiguity, and relevant to research goals Variables measured at the ordinal level are limited to which of the following mathematical operations - Ranking cases as higher, lower, more or less To be converted to % the proportion must be multiplied by - 100 Frequency distributions may be compiled for variables measured at which level? - Ordinal Pie charts show the frequency distr. of - One variable Which of the following can be represented in a bar chart - Counts The 3 commonly used measures of central tendency - Define "typical" or "average" in different ways and will usually have different values.
The most appropriate measure of central tendency for gender would be the - Mode The median defines "central tendency" in terms of the - Central case For ordinal level variables, the most appropriate measure of central tendency is generally - Median For variables measured at the interval-ratio level, the preferred measure of central tendency would be the - Quartile In any distribution, the mean and the median will have the same value when the distribution is - Symmetrical In a positively skewed distribution, the mean is - Less in value than the median Measures of dispersion provide information about the - Variety within the distribution of scores One problem with the range as the measure of dispersion is that it - Is based on only the most extreme scores The second quartile is equal to the - Median The distances between the scores and the mean are called - Deviations Since computation of the standard deviation requires addition, division, and other mathematical operations, it should be used for - Interval- ratio variables only The distribution of scores become more variable, the value of the standard deviation - Increases
Suppose the researchers think a 99% confidence level would be more appropriate for this interval. Will this new interval be smaller, larger, or the same compared to the 95% confidence interval? - Larger When conducting hypothesis tests for two sample means, the test statistic is - The difference in sample means For testing the difference between two sample means, the level of measurement is assumed to be - Interval-ratio When testing for the significance of the difference between two sample means, which of the following is almost always unknown - The population standard deviations When testing for the significance of the difference between sample means with small samples, the proper sampling distribution is - The t distribution For all tests of hypothesis, the probability of rejecting the null hypothesis is a function of - The size of the observed differences, the alpha level and the use of one-or two-tailed tests, and sample size The higher the alpha level - The greater the probability of rejecting the null hypothesis The value of all test statistics increases as___ increases - Sample size When random samples are drawn so that the selection of a case for one sample has no effect on the selection of cases for another sample, the samples are - Independent In hypothesis testing, the ___ is the critical assumption, the assumption which is actually tested - Null hypothesis
Which of the following is NOT an assumption required to a test of hypothesis with a single sample mean? - Sample size (n) larger than 1, A one tailed test of significance could be used whenever - The researcher can predict a direction for the difference If we reject a null hypothesis, which is, in fact, true, we - Have made a type 1 error The probability of a type 1 error is - The alpha level When testing the significance of the difference between a sample mean and a population mean, degrees of freedom are equal to - N- If the test statistic does not fall in the critical region, we - Fail to reject the null hypothesis The ANOVA test is designed for independent variables that have been measured at - The nominal level ANOVA is appropriate for situations in which - We are comparing more than two samples Stated generally, the null hypothesis for the ANOVA test is - μ1 = μ2 = μ3 = ... = μk If we reject the null hypothesis in a test using analysis of variance, we are concluding that - The populations from which our samples come are different The quantity SSW measures the amount of variation - Within categories The ANOVA test is most appropriate for dependent variables that are - Interval-ratio
Some potential difficulties arise in the Chi square test when - Sample size is small, sample size is very large, many cells have expected frequencies of five or less. Scatter plot - Graphic display device that depicts the relationship between 2 variables Regression line - The single best fitting straight line that summarizes the relationship between 2 variables. They're fitted to the data points by the least-squares criterion, whereby the line touches all conditional means of Y or comes as close to doing so as possible. linear relationship - A relationship between 2 variables in which the observation points (dots) in the scatterplot can be approximated with a straight line. Y' - Symbol for predicted score on Y Conditional mean of Y - The mean of all scores on Y for each value of X Y intercept (a) - The point where the regression line crosses the Y axis Slope (b) - The amount of change in one variable per unit change in the other; b is the symbol for the slope of a regression line. Pearson's r - A measure of association for variables that have been measured at the interval-ratio level coefficient of determination - The proportion of all variation in Y that is explained by X. Found by squaring the value of Pearson's r. Total variation - the spread of the Y scores around the mean of Y
explained variation - the proportion of all variation in Y that is attributed to the effect of X Unexplained variation - The proportion of the total variation in Y that is not accounted for by X correlation matrix - A table that shows the correlation coefficients between all possible pairs of variables. Dummy variables - A nominal-level variable dichotomized so that it can be used in regression analysis. Has two scores, one coded as 0 and the other as 1 Bivariate normal distributions - The model assumption in the test of significance for Pearson's r that both variables are normally distributed. Homoscedasticity - The model assumption in the test of significance for Pearson's r that the variance of the Y scores is uniform across all values of X
