MMC 3420 MAIN EXAMS TEST PAPER QUESTIONS
AND ANSWERS SURE A+
✔✔dip is on the left - ✔✔Negative skew
✔✔dip is on the right - ✔✔Positive skew
✔✔the peak is higher; the data is more concentrated - ✔✔Leptokurtic
✔✔the peak is flatter
Data is less concentrated around a central value, is more spread out. - ✔✔Platykurtic
✔✔Arithmetic average.
-Sum of scores divided by the number of scores.
-Takes all scores into account
Provides the most data - ✔✔Mean
✔✔divides the distribution exactly in half
It's the 50th percentile - ✔✔Median
, ✔✔most frequently occurring (popular) score.
Can also be used to describe nominal data. - ✔✔Mode
✔✔how data are spread out around the central point - ✔✔Dispersion
✔✔3 measures of dispersion: - ✔✔Range, Variance, Standard Deviation
✔✔highest and lowest scores; only takes 2 scores into account - ✔✔Range
✔✔how data are distributed around the mean - ✔✔Variance
✔✔the typical, or standard, or average deviation between individual scores in a
distribution and the mean of a distribution.
-This can tell us a lot about distribution of scores on a variable - ✔✔Standard deviation
✔✔What information goes into standard deviation calculations?
What do standard deviations units tell us about scores in a normal distribution? - ✔✔-
First, calculate the deviation score
Subtract the mean from any
given X value
-Second, square each deviation score then calculate the mean of these
this mean is the variance
-Third, the SD is the square root of the variance (above)
-SD is reported in the original units that the mean is reported in
-Allows for comparisons between different types of scales
-You can meaningfully interpret one score within a distribution by showing how many
SD's it is away from the distribution
✔✔How to calculate a z-score and how it relates to standard deviation, the normal
curve and probabilities. - ✔✔One type of standardized score often used by researchers
is the Z-score
Z score of 1 = 1 standard
deviation
Z = X (any variable) - M (the mean), divided by the SD (standard deviation)
AND ANSWERS SURE A+
✔✔dip is on the left - ✔✔Negative skew
✔✔dip is on the right - ✔✔Positive skew
✔✔the peak is higher; the data is more concentrated - ✔✔Leptokurtic
✔✔the peak is flatter
Data is less concentrated around a central value, is more spread out. - ✔✔Platykurtic
✔✔Arithmetic average.
-Sum of scores divided by the number of scores.
-Takes all scores into account
Provides the most data - ✔✔Mean
✔✔divides the distribution exactly in half
It's the 50th percentile - ✔✔Median
, ✔✔most frequently occurring (popular) score.
Can also be used to describe nominal data. - ✔✔Mode
✔✔how data are spread out around the central point - ✔✔Dispersion
✔✔3 measures of dispersion: - ✔✔Range, Variance, Standard Deviation
✔✔highest and lowest scores; only takes 2 scores into account - ✔✔Range
✔✔how data are distributed around the mean - ✔✔Variance
✔✔the typical, or standard, or average deviation between individual scores in a
distribution and the mean of a distribution.
-This can tell us a lot about distribution of scores on a variable - ✔✔Standard deviation
✔✔What information goes into standard deviation calculations?
What do standard deviations units tell us about scores in a normal distribution? - ✔✔-
First, calculate the deviation score
Subtract the mean from any
given X value
-Second, square each deviation score then calculate the mean of these
this mean is the variance
-Third, the SD is the square root of the variance (above)
-SD is reported in the original units that the mean is reported in
-Allows for comparisons between different types of scales
-You can meaningfully interpret one score within a distribution by showing how many
SD's it is away from the distribution
✔✔How to calculate a z-score and how it relates to standard deviation, the normal
curve and probabilities. - ✔✔One type of standardized score often used by researchers
is the Z-score
Z score of 1 = 1 standard
deviation
Z = X (any variable) - M (the mean), divided by the SD (standard deviation)
