(Answered) Stats Sophia 5, Statistics Sophia 4,
Statistics Sophia 2, Statistics Sophia 1.1 Exam/
300 Q&A.
sample statistic - Answer: A measure of an attribute of a sample.
sample mean - Answer: A mean obtained from a sample of a given size. Denoted as x bar.
population parameters - Answer: Summary values for the population. These are often
unknown.
F statistic - Answer: The test statistic in an ANOVA test. It is the ratio of the variability between
the samples to the variability within each sample. If the null hypothesis is true, the F statistic
will probably be small.
Page 1 of 39
,one-way ANOVA - Answer: A hypothesis test that compares three or more population means
with respect to a single characteristic or factor.
two-way ANOVA - Answer: A hypothesis test that compares three or more population means
with respect to multiple characteristics or factors.
chi-square statistic - Answer: Take the observed values.
Subtract the expected values.
Square that difference.
Divide by the expected values.
Add up all of those fractions.
The sum of the ratios of the squared differences between the expected and observed counts to
the expected counts.
observed frequencies - Answer: The number of occurrences that were observed within each of
the categories in a qualitative distribution.
expected frequencies - Answer: The number of occurrences we would have expected within
each of the categories in a qualitative distribution if the null hypothesis were true.
chi-square test for goodness-of-fit - Answer: Step 1: State the null and alternative hypotheses.
Step 2: Check the conditions.
Step 3: Calculate the test-statistic and p-value.
Step 4: Compare your test statistic to your chosen critical value, or your p-value to your chosen
significance level. Based on how they compare, state a decision about the null hypothesis and
conclusion in the context of the problem.
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,A hypothesis test where we test whether or not our sample distribution of frequencies across
categories fits with hypothesized probabilities for each category.
chi-square test of homogeneity - Answer: Step 1: State the null and alternative hypotheses.
Step 2: Check the conditions.
Step 3: Calculate the test-statistic and p-value.
Step 4: Compare your test statistic to your chosen critical value, or your p-value to your chosen
significance level. Based on how they compare, state a decision about the null hypothesis and
conclusion in the context of the problem.
chi-square test for association - Answer: Step 1: State the null and alternative hypotheses.
Step 2: Check the conditions.
Step 3: Calculate the test-statistic and p-value
Step 4: Compare your test statistic to your chosen critical value, or your p-value to your chosen
significance level. Based on how they compare, state a decision about the null hypothesis and
conclusion in the context of the problem.
scatterplot - Answer: A graphical display that allows us to see the relationship between two
quantitative variables.
multiple data sets - Answer: Plotting more than one data set on a scatterplot requires that we
use different colors or symbols for the different data sets so we can see the relationships
separately.
form - Answer: The overall shape of the data points. The form may be linear or nonlinear, or
there may not be any form at all to the points if they form a "cloud."
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, direction - Answer: The way one variable responds to an increase in the other. With a negative
association, an increase in one variable is associated with a decrease in the other, whereas with
a positive association, an increase in one variable is associated with an increase in the other.
strength - Answer: The closeness of the points to the indicated form. Points that are strongly
linear will all fall on or near a straight line.
explanatory variable - Answer: The variable whose increase or decrease we believe helps
explain a tendency to increase or decrease in some other variable.
response variable - Answer: The variable that tends to increase or decrease due to an increase
or decrease in the explanatory variable.
correlation - Answer: The strength and direction of a linear association between two
quantitative variables.
correlation coefficient - Answer: The numerical value between -1 and +1 that measures the
correlation between two quantitative variables.
positive correlation - Answer: The type of correlation present when two variables have a
correlation coefficient generally greater than or equal to 0.5.
negative correlation - Answer: The type of correlation present when two variables have a
correlation coefficient generally less than or equal to -0.5.
Relative Zero Correlation - Answer: The type of correlation present when two variables have a
correlation coefficient generally between -0.5 and 0.5.
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Statistics Sophia 2, Statistics Sophia 1.1 Exam/
300 Q&A.
sample statistic - Answer: A measure of an attribute of a sample.
sample mean - Answer: A mean obtained from a sample of a given size. Denoted as x bar.
population parameters - Answer: Summary values for the population. These are often
unknown.
F statistic - Answer: The test statistic in an ANOVA test. It is the ratio of the variability between
the samples to the variability within each sample. If the null hypothesis is true, the F statistic
will probably be small.
Page 1 of 39
,one-way ANOVA - Answer: A hypothesis test that compares three or more population means
with respect to a single characteristic or factor.
two-way ANOVA - Answer: A hypothesis test that compares three or more population means
with respect to multiple characteristics or factors.
chi-square statistic - Answer: Take the observed values.
Subtract the expected values.
Square that difference.
Divide by the expected values.
Add up all of those fractions.
The sum of the ratios of the squared differences between the expected and observed counts to
the expected counts.
observed frequencies - Answer: The number of occurrences that were observed within each of
the categories in a qualitative distribution.
expected frequencies - Answer: The number of occurrences we would have expected within
each of the categories in a qualitative distribution if the null hypothesis were true.
chi-square test for goodness-of-fit - Answer: Step 1: State the null and alternative hypotheses.
Step 2: Check the conditions.
Step 3: Calculate the test-statistic and p-value.
Step 4: Compare your test statistic to your chosen critical value, or your p-value to your chosen
significance level. Based on how they compare, state a decision about the null hypothesis and
conclusion in the context of the problem.
Page 2 of 39
,A hypothesis test where we test whether or not our sample distribution of frequencies across
categories fits with hypothesized probabilities for each category.
chi-square test of homogeneity - Answer: Step 1: State the null and alternative hypotheses.
Step 2: Check the conditions.
Step 3: Calculate the test-statistic and p-value.
Step 4: Compare your test statistic to your chosen critical value, or your p-value to your chosen
significance level. Based on how they compare, state a decision about the null hypothesis and
conclusion in the context of the problem.
chi-square test for association - Answer: Step 1: State the null and alternative hypotheses.
Step 2: Check the conditions.
Step 3: Calculate the test-statistic and p-value
Step 4: Compare your test statistic to your chosen critical value, or your p-value to your chosen
significance level. Based on how they compare, state a decision about the null hypothesis and
conclusion in the context of the problem.
scatterplot - Answer: A graphical display that allows us to see the relationship between two
quantitative variables.
multiple data sets - Answer: Plotting more than one data set on a scatterplot requires that we
use different colors or symbols for the different data sets so we can see the relationships
separately.
form - Answer: The overall shape of the data points. The form may be linear or nonlinear, or
there may not be any form at all to the points if they form a "cloud."
Page 3 of 39
, direction - Answer: The way one variable responds to an increase in the other. With a negative
association, an increase in one variable is associated with a decrease in the other, whereas with
a positive association, an increase in one variable is associated with an increase in the other.
strength - Answer: The closeness of the points to the indicated form. Points that are strongly
linear will all fall on or near a straight line.
explanatory variable - Answer: The variable whose increase or decrease we believe helps
explain a tendency to increase or decrease in some other variable.
response variable - Answer: The variable that tends to increase or decrease due to an increase
or decrease in the explanatory variable.
correlation - Answer: The strength and direction of a linear association between two
quantitative variables.
correlation coefficient - Answer: The numerical value between -1 and +1 that measures the
correlation between two quantitative variables.
positive correlation - Answer: The type of correlation present when two variables have a
correlation coefficient generally greater than or equal to 0.5.
negative correlation - Answer: The type of correlation present when two variables have a
correlation coefficient generally less than or equal to -0.5.
Relative Zero Correlation - Answer: The type of correlation present when two variables have a
correlation coefficient generally between -0.5 and 0.5.
Page 4 of 39
