Term
| 1. The mean of the sampling distribution of pˆ. (p-hat) |
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Definition
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Term
| 2. The standard deviation of the sampling distribution of pˆ. (p-hat) |
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Definition
| 2. What is the square root of p (1-p) over n. |
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Term
| 3. The standard error of pˆ (p-hat) used in a confidence interval. |
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Definition
| 3. What is the square root of p-hat (1-p-hat) over n. |
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Term
4. When testing H0: p = 0.8 with n = 100, the value of the standard deviation of pˆ (p-hat) assuming the null
is true. |
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Definition
| 4. What is the square root of p (1-p) over n = the square root of .8(1-.8) over 100 =.04 |
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Term
5. The shape of the sampling distribution of pˆ (p-hat) when the sample is large (i.e., np ≥ 10 and n(1 – p)≥
10) and random. |
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Definition
| 5. What is approximately Normal. |
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Term
| 1. z* times the square root of p-hat times (1-pˆ[p-hat]) over / n |
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Definition
| 1. What is the formula for margin of error for estimating population proportion, p. |
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Term
| 2. SRS and np0 (p-naught) ≥ 10 and n(1 – p0 [p-naught]) ≥ 10. |
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Definition
| 2. What are the checks you need to make when testing H0: p = p0. |
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Term
| 3. SRS and npˆ ≥ 10 and n(1 – pˆ) ≥ 10. |
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Definition
| 3. What are the checks you need to make when constructing a confidence interval for p. Note: “10” is the number to remember for the final. You actually should use “15”. |
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Term
4. Another name for the marginal proportion of success in a 2x2 two-way table used in the
denominator of the two-sample z test statistic for proportion. |
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Definition
| 4. What is pooled sample proportion. |
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Term
| 5. np ≥ 10 and n(1 – p) ≥ 10. |
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Definition
| 5. What are the checks to determine whether the sampling distribution of pˆ has an approximately Normal shape. |
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Term
| 1. The probability of getting a value of the test statistic as extreme or more extreme than the value actually observed assuming H0 is true. |
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Definition
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Term
| 2. How P-value and α compare when results are declared statistically significant. |
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Definition
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Term
| 3. The conditional clause in a correct definition of P-value. |
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Definition
| 3. What is “If H0 is true.” |
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Term
| 4. How you determine whether results of a test are statistically significant. |
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Definition
| 4. What is checking whether P-value < α. |
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Term
| 5. How you determine whether results of a test are also practically significant. |
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Definition
| 5. What is checking the numerator of the test statistic and asking if the difference is important or has meaning. |
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Term
6. A difference between the observed statistic and the claimed parameter value that is too large to be
due to chance. |
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Definition
| 6. What is statistically significant. |
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Term
| 7. The hypothesis that is assumed to be true until sample results indicates otherwise. |
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Definition
| 7. What is H0, the null hypothesis. |
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Term
| 8. The hypothesis that the researcher usually wants to prove. |
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Definition
| 8. What is Ha, the alternative hypothesis. |
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Term
| 9. What is checked for practical significance. |
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Definition
| 9. What is the numerator of the test statistic. |
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Term
| 10. The probability that the null hypothesis is true. |
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Definition
| 10. What is zero or one depending on whether the null is correct or not. This is a misconception. |
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Term
| 11. How P-value and α compare when results are declared NOT statistically significant. |
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Definition
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Term
| 12. How P-value and α compare when results are declared NOT statistically significant. |
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Definition
| 12. What is H0, the null hypothesis. |
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Term
| 13. The probability of obtaining a value of the test statistic as extreme or more extreme than observed if H0 were true. |
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Definition
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Term
| 14. The conditions under which we check for practical significance. |
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Definition
| 14. What is whether the test is significant. |
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Term
| 15. The probability of rejecting a false null hypothesis. |
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Definition
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Term
| 1. The maximum amount that a statistic will differ from the value of the parameter it estimates for the middle (1 – C)x100% of the statistics. |
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Definition
| 1. What is margin of error. |
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Term
| 2. An estimate of a parameter in interval form with an associated level of confidence. |
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Definition
| 2. What is a confidence interval. |
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Term
| 3. A range of reasonable values for the population parameter being estimated. |
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Definition
| 3. What is a confidence interval. |
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Term
| 4. The percent of the time that the confidence interval estimation procedure gives confidenceintervals that contain the value of the parameter. |
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Definition
| 4. What is level of confidence. |
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Term
| 5. The value found in a confidence interval that leads to failing to reject H0. |
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Definition
| 5. What is the claimed parameter value. |
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Term
| 1. The name of s over the square of n. |
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Definition
| 1. What is standard error of x-bar. |
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Term
| 2. An estimate of the standard deviation of the sampling distribution of x-bar. |
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Definition
| 2. What is s over the square root of n , the standard error of x-bar. |
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Term
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Definition
| 3. What is level of significance. |
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Term
| 4. An estimate of the standard deviation of the sampling distribution of pˆ (p-hat). |
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Definition
| 4. What is the square root of p-hat(1-p-hat ) over n, , the standard error of pˆ(p-hat). |
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Term
| 5. The symbol for the mean of the sample of differences in a matched pairs t procedure. |
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Definition
| 5. What is d-bar or x-bar. |
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Term
| 6. The standard error of x-bar 1 minus x-bar 2. |
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Definition
| 6. What is the square root of s 1 squared over n 1 plus s 2 squared over n 2. |
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Term
| 1. H0: μ1 = μ2 or H0: μ1 – μ2 = 0 |
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Definition
| 1. What is the null hypothesis for a two-sample t test. |
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Term
| 2. The smaller of n1 – 1 and n2 – 1. |
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Definition
| 2. What are degrees of freedom for a conservative two-sample t test. |
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Term
| 3. The value you look for in a confidence interval for μ1 – μ2 in order to test H0: μ1 = μ2. |
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Definition
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Term
| 4. The square root of s 1 squared over n 1 plus s 2 squared over n 2. |
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Definition
| 4. What is the standard error of x-bar 1 minus x-bar 2. |
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Term
| 5. When to use a two-sample t procedure instead of a matched pairs t. |
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Definition
| 5. When the two samples are independent (completely separate). |
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Term
| 1. All expected counts are greater than or equal to 5. |
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Definition
| 1. What is the size that the expected counts need to be for appropriately performing a chi-square test? |
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Term
| 2. H0: p 1 = p 2 = p 3 = p 4 versus Ha: not all proportions are equal. |
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Definition
| 2. What are the hypotheses for chi-square test of homogeneity for comparing equality of three proportions? |
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Term
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Definition
| 3. What are the degrees of freedom for a chi-square test? |
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Term
| 4. H0: No association between the explanatory and response variables versus Ha: Association between explanatory and response variables. |
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Definition
| 4. What are the hypotheses for chi-square test of independence? |
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Term
| 5. Row Total times Column Total over Table Total computed assuming no association between row and column variables. |
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Definition
| 5. What is the expected count? |
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Term
| 1. An analysis procedure for comparing equality of three or more means. |
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Definition
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Term
| 2. H0: μ1 = μ2 = μ3 = μ4 versus Ha: not all means are equal. |
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Definition
| 2. What are the hypotheses for comparing four means in an ANOVA procedure. |
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Term
| 3. The largest standard deviation divided by the smallest standard deviation is less than 2. |
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Definition
| 3. What is the check for the equal variance condition in ANOVA. |
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Term
| 4. Random allocation of individuals to treatments or random selection of individuals from independent populations. |
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Definition
| 4. What are two ways of appropriate data collection for ANOVA. |
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Term
| 5. A confidence interval for μA and a confidence interval for μB that do not overlap. |
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Definition
| 5. What are two confidence intervals giving evidence that μA and μB differ significantly. |
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Term
| 1. A megaphone pattern in the residual plot. |
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Definition
| 1. What indicates a violation of equal variance condition for inference in regression in a residual plot? |
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Term
| 2. Time in minutes that an icicle has grown explains 99.2% of the variability in icicle length. |
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Definition
| 2. What is an interpretation of r2 in context for the relationship between time in minutes that an icicle grows and the length of the icicle. |
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Term
| 3. The line with the minimum sum of square residuals. |
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Definition
| 3. What is the least squares line. |
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Term
| 4. A shoe-box pattern in a residual plot. |
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Definition
| 4. What is the pattern in a residual plot indicating no violations of conditions for inference in regression? |
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Term
| 5. Confidence interval for the mean of the y’s at x* is narrower than the prediction interval for an individual y at x*. |
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Definition
| 5. What is how a confidence interval for the mean of the y’s at x* compare with a prediction interval for an individual y at x*. |
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Term
| 6. Regression symbols α and β. |
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Definition
| 6. What are parameter symbols for the true y-intercept and true slope. |
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Term
| 7. A measure of the variation of the y’s about the regression line. |
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Definition
| 7. What is “s” in a regression output. |
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Term
| 8. Estimated slope plus or minus t* (standard error of slope). |
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Definition
| 8. What is the formula for confidence interval for slope. |
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Term
| 9. Velocity increases by 274 feet per second on average for every one inch increase in thickness of the cylinder wall. |
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Definition
| 9. What is an interpretation of slope in context. |
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Term
| 10. Regression symbols: a and b. |
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Definition
| 10. What are symbols for estimated y-intercept and slope. |
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Term
| 1. A study that establishes a cause and effect relationship between the explanatory and response variables. |
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Definition
| 1. What is a comparative experiment with randomization and replication? |
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Term
| 2. Appropriate statistical conclusion when using the 95% confidence interval for μ1 – μ 2, namely, using the interval (–2.23, 1.17) to test H0: μ1 – μ2 = 0. |
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Definition
| 2. What is failing to reject the null hypothesis since zero is contained in the interval. |
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Term
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Definition
| 3. What is the parameter for comparing two population means. |
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Term
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Definition
| 4. What is the parameter for comparing two population proportions. |
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Term
| 5. Procedure for analyzing data where both the explanatory variable and the response variable are categorical and one or the other has three or more categories. |
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Definition
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Term
| 6. Procedure for analyzing data where the explanatory variable is categorical with three or more categories and the response variable is quantitative. |
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Definition
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Term
| 7. Procedure for analyzing data where both the explanatory variable and the response variable are quantitative. |
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Definition
| 7. What is regression analysis. |
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Term
| 8. Procedure for analyzing data where the explanatory variable is categorical with only two categories and the response variable is quantitative. |
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Definition
| 8. What is a two-sample t procedure. |
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Term
| 9. Procedure for analyzing data where both the explanatory variable and the response variable are categorical and both have only two categories. |
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Definition
| 9. What is a two-sample z procedure for proportion. |
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Term
| 10. Random allocation of individuals to treatments or random selection of individuals from independent populations. |
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Definition
| 10. What are the two appropriate methods of data collection for inference. |
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