• Parameter: describes characteristics or measures of the population
• Statistic: applies to the selected sample

• the average of a group of numbers and is calculated by adding the scores together and dividing by the number of scores.
• The mean can be visualized as the balance point in your scores.

• a way to determine how "spread out" your data points are from the mean.
• Use the term "sum of squares" (SS_x) as a measure of this spread.
• Formula: SS_x / (n-1)

• The square root of the variance.
• This is another way to describe how "spread out" your data points are.

• supplies an approximayion of the true value's population parameters.

• says that for any population, regardless of its distribution, the distribution of your sample approaches a normal distribution as your sample size.

• When you generalize your sample results to your population results.
• Statistics, such as the sample mean and sample proportion, are used to Truth the corresponding true population values

• measure the standard deviations from the mean of normal distribution, the z value for the desired confidence proportion can be applied to the standard error to construct the confidence interval.
• The z value refers to the area under the sampling distribution between any two points.

• tell us how far away a specific score is from the mean.
• z scores describe two things: signs which indicate above or below the mean, and the number which describes the distance from the mean counting in standard deviation.

• Descriptive
• Inferential
•  Use stats for: income, education, age
• Use frequency analysis for: gender, location, race

• The objective of using frequencies output is to get a count of the number of responses associated with different values of a certain factor (i.e. purchase intention) of interest.
• used with measures of location, measures of variability, and measures of shape.

• mean: the average of the responses. Interval scaled.
• mode: the most commonly given responses. Nominal scaled.
• median: the middle value of the data when data are arranged in an ascending or descending order. Ordinal scaled.

• a statistical examination of two or more variables which are described simultaneously and result in a contingency table reflecting the joint distribution of the variables.

• used to evaluate the success of your manipulation
• Reasons why Crosstabs are popular: Clarity of its interpretations, easy comprehension of its results, provides greater insights than a single multivariate analysis, and simple to conduct & important for manipulation checks.

• Stength: measured by the chi-square statistic
• Significance: measured by the phi-correlation coefficient, the contingency coefficient, Cramer's V, and/or the Lambda Coefficient.

• To determine whether a systematic association exists, the probability of obtaining a value of chi-square as large as or larger than the one calculated from the cross tribulation is estimated.
• only conveys the existence or nonexistence of the relationships between the factors being investigated. (comparing counts, not means)

• Null Hypothesis: the researcher is saying that you are expect (1) no difference or (2) no effect.
• Alternative Hypothesis: the researcher is saying you do expect a difference or an effect.

• Type I: this occurs when you reject the null hypothesis when it is actually true.
• Type II: this occurs when you do not reject the null hypothesis when it is in fact false.

• assume that the variables of interest are measured on at least an interval scale.
• The t-test used for examining the hypotheses about means.

• the objective is to test the mean of a factor of interest (one of your DVs) against a pre-specified value.

• 2 independent samples the point of interest is in testing whether the population means conform to H₀

• assume that the variables are measured on a nominal or ordinal scale, thus no distributional assumptions are needed.
• rely on randomization

• these are useful for testing differences between two or more means whereas cross-tabs and t-tests are applicable only to cases involving two means.
• 2x2 experimental design tests 4 means
• ANOVA tests are applicable when all of the independent variables are categorical.
• ANCOVA is used when some independent variables are categorical and some metric
• ANOVA tests for differences between IVs (AKA groups)

• Tests for relationships (i.e. correlations) between IVs.

• eta² = the strength of the effects of X (your IVs) on Y (on DVs). It varies between 0 and 1.
• F-statistic = the statistic used to test the null hypothesis that the category means (AKA the averages from your IVs) are equal in the population.
• Mean Square = the sum of squares (SS) divided by the degrees of freedom
• SS_between = the variation in Y due to the variation in the means of the categories of X. It represents variation between the categories of X.
• SS_within = the variation in Y due to the variation within each of the categories of X. It is not accounted for by X.
• SS_y = this is the total variation in Y.

• The null hypothesis is tested by the F statistic. If the null hypothesis is not rejected, then the independent variable does not have a significant effect on the dependent variable. If insted the null hypothesis is rejected, then the IV does have a significant effect on the dependent variable.
• If one-way ANOVA is used and needed to use a t-test in addition then you would use an independent samples t-test.

• requires at least one categorical independent variable (factor), at least one interval independent variable (covariate), and at least one interval dependent variable.
• ANCOVA turns out to be most useful when the covariate is linearly related to the dependent variable and is not related to the factors (i.e. IVs)

• The first thing you ask is "what group am i interested in?"
• Sampling frames three critical properties: critical, complete, and up to date. Without these will likely result in understanding your target population and biased survey results.
• Choose between probability and non probability sampling

The Mathematical symbols "σ_X" and S_X" represent a ____ for the population and the sampling distribution respectively

________

Eta²(n²)

Independent Sample; Reject

If you are estimating n², you are at which step in the procedure for conducting one-way analysis of variance?