Master this deck with 110 terms through effective study methods.
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To measure how the values of two variables move relative to each other.
Independent data values can also be used.
To check for extreme values and unusual patterns.
A lurking variable could affect the results.
To see any relationship between two variables.
The response variable.
The variable that changes first is the predictor.
To summarize the linear component between two variables.
The strength and direction of the linear relationship.
To determine the effect of one variable on another.
To find the direction and strength of the linear relationship.
The Least Squares method.
Yes, because both variables can be measured from one individual.
No, as statistics cannot determine causation.
The movement between the data values.
The statistical results may lack meaning.
Correlation does not imply causation.
Outside of the variables measured.
It can affect statistical results without being known.
How one variable's variance changes with the other.
Any linear relationship between the two variables.
Only a linear relationship.
Yes, it measures the strength of the linear relationship.
-1 to +1, including the endpoints.
The size of the effect of the predictor variable.
By the equation of the line of best fit.
To measure the linear relationship between two variables.
Linear, curvilinear, and no relationship.
Data values move together or opposite.
Peaks, gaps, and extreme values.
They can skew statistical results.
To measure the linear component of data pairs.
It provides a clear summary of variable relationships.
Standardize the raw data values into z-scores.
r = 0.
The mathematical sign of the correlation coefficient.
Only the linear component of the relationship.
As dots outside the overall pattern.
Look at the overall pattern and exceptions.
The Rubber-Band method.
Variables move together or opposite.
The one with points closer to a line.
One linear relationship with extreme values.
One may show a linear relationship while the other does not.
Mathematical methods quantify relationships.
Multiplying z-scores and summing.
Nearly equal numbers of positive and negative signs.
Direction and strength.
Values indicating the strength and direction of relationships.
By forming a long, narrow oval shape.
The one with points farther from a straight line.
It can skew the results significantly.
Cannot be determined from the information given.
It represents the balance point of data values.
It helps find the relationship between two columns.
It allows for explanation and prediction.
The regression line.
A simple regression.
The size of the effect on the response variable.
In the equation of the regression line.
y = mx + b.
y-intercept = b0, slope = b1.
They are normally distributed around the regression line.
If x goes up one unit, then y goes up by the slope.
If x equals a certain value, then predict y.
The vertical distance from the point to the regression line.
Residual = 0.
One predictor and one response variable.
Data values must be independent and normally distributed.
Both the y-intercept and the slope.
Valid for interpolation inside the range of the predictor variable.
The point with the smallest residual.
By minimizing the sum of the squared residuals.
Weight would go up by the slope value.
Weight would go up by 12 times the slope.
Weight would go down by six times the slope.
Weight would go up by twelve times the slope.
Calculate using the regression equation.
Calculate using the regression equation.
Compare to the predicted price from the regression.
Calculate using the regression equation.
Price cannot be negative in economic terms.
The difference between the observed and predicted values.
A deviation.
Residual = observed value - predicted value.
Positive and negative residuals cancel out.
By minimizing the sum of squared residuals.
Find the slope and y-intercept using least squares.
Indicates the direction of data movement.
CV = ±1.64.
Reject or do not reject the null hypothesis based on p-value.
Using a normal curve to approximate a binomial histogram.
Calculate using binomial probability.
Calculate using binomial probability.
Calculate using binomial probability.
Calculate using binomial probability.
Calculate using binomial probability.
Calculate using binomial probability.
To determine if the two variables are independent.
No, it can only be used with categorical data.
Probability information.
Yes, as it is a hypothesis test.
(0, ∞) / Skewed right.
The deviation between observed and expected frequencies.
A hypothesis test for two binomial variables.
The two variables are independent.
The chi-square table includes expected frequencies.
The tail area from the test statistic to positive infinity.
Dependent, because the p-value is low.
Related, because the p-value is low.