Term
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Definition
| Two measurements are made on each unit |
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Definition
| The list of every unit in a population |
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Term
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Definition
| A contingency table where the counts in two (or more) rows or two (or more) columns have been added to form a single row or column |
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Term
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Definition
| A mathematical model used to describe the overall pattern of the distribution of a random variable |
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Term
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Definition
| An estimate of how many observations should be in a cell of a two way table if there were no association between the row and column variables |
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Definition
The amount of total variation in the y's that is accounted for by a regression model
Σ(yhat-ybar)2 |
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Term
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Definition
| The distribution of all Y values at each possible value of X is normal |
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Definition
| The count of individuals actually observed in a given cell of a two way table |
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Term
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Definition
| An interval estimate of plausible values for a single observation of Y at a specified value of X |
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Definition
| The fraction of successes in either a sample (p-hat) or a population (p) |
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Term
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Definition
| The percentage of total variation in y that is explained by x |
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Term
Test of Independence
What is the Null Hypothesis? |
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Definition
| The null hypothesis states that there is no relationship between the two categorical variables |
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Term
| Theoretical Regression Model |
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Definition
The regression equation for the population estimated by the least squares regression line whose slope and y-intercept are found using sample data
μy=α+βx |
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Term
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Definition
| The sum of the squared residuals |
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Term
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Definition
| A measure of the average squared deviation of the data about the mean |
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Term
Conditions for Tests of Significance for Proportions
(3) |
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Definition
Randomization
Large Population (10n<population)
Normality of the Sampling Distribution of p-hat (np>=10 and n(1-p)>=10) |
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Term
Conditions for Chi-Square
(2) |
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Definition
Randomization
All expected counts must be greater than or equal to 5 |
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Term
| Mean and Standard Deviation of the Sampling Distribution of P-hat |
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Definition
Mean: P
Standard Deviation: √(p(1-p)/n) |
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