Statistics

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bekka.wood
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215109
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Statistics
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2013-04-24 01:37:51
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stat 121
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Studly for stats
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  1. Be able to define distribution.
    A list of all possible values of a variable together with the frequency (or probability) of each value.
  2. What is an explanatory variable?
    A variable that may or may not explain the outcomes (responses) of a study, also called independent or predictor variable.
  3. What is a response variable?
    A variable that gives the outcomes of interest of the study (may not be a number); also called the dependent variable.
  4. What is a categorical variable?
    categorical (or qualitative) variable: A variable that can be classified into groups or categories such as gender and religion.
  5. What is a quantitative variable?
    quantitative variable: A variable with numerical values such as height or weight. This type of data required for both variables in regression analysis.
  6. What type of graph does a categorical data need?
    Bar graph, line graphs or pie charts.
  7. What type of graph does a quantitative data need?
    get this answer!
  8. Data where two identical measurements are taken at different times (or under different conditions) on each individual in a sample.
    What is matched pairs
  9. The value for μ0 in the test statistic formula when performing a matched pairs t test.
    What is zero.
  10. The checks you need to make when performing a matched pairs t test
    What are data collection and either plot of differences has no outliers or number of pairs exceeds 40.
  11. What you plot to check for skewness and outliers for a matched pairs t.
    What is plot of differences.
  12. What you need to compute before you can compute the mean and standard deviation for the test statistic.
    What is the difference between each pair
  13. The distribution we use whenever we use sample standard deviations to estimate population standard deviations.
    What is the t distribution
  14. The parameter used when comparing the means from two populations.
    What is mu1 – mu2.
  15. The value you look for in a confidence interval for mu to test H0: mu = 50.
    What is the value of 50.
  16. The value that determines the spread of a t distribution.
    What are degrees of freedom.
  17. The value of the mean of a t-distribution
    What is zero.
  18. The checks you need to make when performing a one-sample t procedure—either test or confidence interval for mu?
    What are data collection (SRS) and plot has no extreme skewness or outliers
  19. The checks you need to make when performing a two-sample t procedure.
    What are appropriate data collection (random allocation or random selection) and neither plot has extreme skewness or outliers.
  20. The mean of the sampling distribution of p-hat. .
    What is p
  21. The shape of the sampling distribution of p-hat when the sample is large (i.e., np ≥ 10 and n(1 – p)≥ 10) and random.
    What is approximately Normal.
  22. s
    What is the formula for margin of error for estimating population proportion, p.
  23. SRS and np0 ≥ 10 and n(1 – p0) ≥ 10.
    What are the checks you need to make when testing H0: p = p0
  24. SRS and n pˆ ≥ 10 and n(1 – pˆ ) ≥ 10
    What are the checks you need to make when constructing a confidence interval for p
  25. 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.
    What is pooled sample proportion
  26. np ≥ 10 and n(1 – p) ≥ 10.
    What are the checks to determine whether the sampling distribution of pˆ has an approximately Normal shape.
  27. The probability of getting a value of the test statistic as extreme or more extreme than the value actually observed assuming H0 is true.
    What is P-value.
  28. How P-value and alpha compare when results are declared statistically significant.
    What is P-value < alpha
  29. The conditional clause in a correct definition of P-value.
    What is “If H0 is true.”
  30. How you determine whether results of a test are statistically significant.
    What is checking whether P-value < alpha
  31. How you determine whether results of a test are also practically significant.
    What is checking the numerator of the test statistic and asking if the difference is important or has meaning.
  32. A difference between the observed statistic and the claimed parameter value that is too large to be due to chance
    What is statistically significant
  33. The hypothesis that is assumed to be true until sample results indicates otherwise.
    What is H0, the null hypothesis
  34. The hypothesis that the researcher usually wants to disprove
    What is Ha, the alternative hypothesis.
  35. What is checked for practical significance.
    What is the numerator of the test statistic
  36. The probability that the null hypothesis is true.
    What is zero or one depending on whether the null is correct or not. This is a misconception
  37. How P-value and alpha compare when results are declared NOT statistically significant.
    What is P-value > alpha
  38. The hypothesis assumed to be true in order to compute P-value.
    What is H0, the null hypothesis.
  39. The probability of obtaining a value of the test statistic as extreme or more extreme than observed if H0 were true.
    What is P-value.
  40. The conditions under which we check for practical significance.
    What is whether the test is significant.
  41. The probability of failing to reject a false null hypothesis.
    What is alpha, the probability of a type I error.
  42. 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.
    What is margin of error.
  43. An estimate of a parameter in interval form with an associated level of confidence.
    What is a confidence interval.
  44. A range of reasonable values for the population parameter being estimated.
    What is a confidence interval.
  45. The percent of the time that the confidence interval estimation procedure gives confidence intervals that contain the value of the parameter.
    What is level of confidence.
  46. The value found in a confidence interval that leads to failing to reject H0
    What is the claimed parameter value.
  47. The name for alpha.
    What is level of significance.
  48. All expected counts are greater than or equal to 5.
    What is the size that the expected counts need to be for appropriately performing a chi-square test?
  49. (r –1) times (c –1)
    What are the degrees of freedom for a chi-square test?
  50. H0: No association between the explanatory and response variables versus Ha: Association between explanatory and response variables.
    What are the hypotheses for chi-square test?
  51. An analysis procedure for comparing equality of three or more means.
    ANOVA
  52. H0: mu1 =mu 2 = mu 3 = mu 4 versus Ha: not all means are equal.
    What are the hypotheses for comparing four means in an ANOVA procedure?
  53. The largest standard deviation divided by the smallest standard deviation is less than 2.
    What needs to be checked for the equal variance condition in ANOVA?
  54. Random allocation of individuals to treatments or random selection of individuals from independent populations.
    What are two ways of appropriate data collection for ANOVA?
  55. Confidence intervals for two means that do not overlap.
    What are two confidence intervals giving evidence that their two means differ significantly?
  56. A megaphone pattern in the scatterplot
    What indicates a violation of equal variance condition for inference in regression?
  57. Time in minutes that an icicle has grown explains 99.2% of the variability in icicle length.
    What is an interpretation of r2in context for the relationship between time in minutes that an icicle grows and the length of the icicle?
  58. The line with the minimum sum of square residuals.
    What is the least squares line?
  59. A shoe-box pattern in a scatterplot.
    What is the pattern in a scatterplot indicating no violations of conditions for inference in regression?
  60. Confidence interval for the mean of the y’s at x* is narrower than the prediction interval for an individual y at x*.
    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*?
  61. Regression symbols alpha and beta.
    What are parameter symbols for the true y-intercept and true slope?
  62. Remove variation associated with the blocking variable from the experimental error.
    What is the advantage of a randomized block design over a completely randomized design?
  63. Estimated slope +/- t*(Standard error of slope)
    What is the formula for confidence interval for slope?
  64. Velocity increases by 274 feet per second on average for every one inch increase in thickness of the cylinder wall.
    What is an interpretation of slope in context?
  65. Regression symbols: a and b.
    What are symbols for estimated y-intercept and slope?
  66. Establish a cause and effect relationship between the explanatory and response variables.
    What is why we perform a comparative experiment with randomization and replication?
  67. The results of using a 95% confidence interval for 1 –  2, namely, (–2.23, 1,17) to test H0: 1 –  2 = 0.
    What is failing to reject the null hypothesis since zero is contained in the interval?
  68. MU1 – MU 2.
    What is the parameter for comparing two population means?
  69. p1 – p2.
    What is the parameter for comparing two population proportions?
  70. 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.
    What is chi-square?
  71. Procedure for analyzing data where the explanatory variable is categorical with three or more categories and the response variable is quantitative.
    What is ANOVA?
  72. Procedure for analyzing data where both the explanatory variable and the response variable are quantitative.
    What is regression analysis?
  73. Procedure for analyzing data where the explanatory variable is categorical with only two categories and the response variable is quantitative.
    What is a two-sample t procedure?
  74. Procedure for analyzing data where both the explanatory variable and the response variable are categorical and both have only two categories.
    What is a two-sample z procedure for proportions?
  75. Random allocation of individuals to treatments or random selection of individuals from independent populations
    What are the two appropriate methods of data collection for inference?

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