Learning Outcomes - Winona State University
... List the advantages and disadvantages of a Brookfield type viscometer. Name the applications of Brookfield viscometers in composites industry. Measure the viscosity of a number of various mixtures of two oils at different temperatures and develop a mathematical model to describe the data. Use multi ...
... List the advantages and disadvantages of a Brookfield type viscometer. Name the applications of Brookfield viscometers in composites industry. Measure the viscosity of a number of various mixtures of two oils at different temperatures and develop a mathematical model to describe the data. Use multi ...
intervals. Given the more parsimonious model Although sociologists
... This test can also be utilized to test the equality of constant terms in case the variables are not measured in deviation or standard deviation units. For such a test, one merely enters the dummy variable into the equation in an additive fashion. In the present context, the method can also be genera ...
... This test can also be utilized to test the equality of constant terms in case the variables are not measured in deviation or standard deviation units. For such a test, one merely enters the dummy variable into the equation in an additive fashion. In the present context, the method can also be genera ...
Types of Descriptive/Correlational Research
... t-test: used to compare two group means to see if they are "significantly*" different from one another. ...
... t-test: used to compare two group means to see if they are "significantly*" different from one another. ...
Statistics involves collecting, organizing, analyzing, and interpreting
... Press Graph. The scatter plot will appear. Describe the shape of the graph. c) Perform a linear regression analysis. Press 2nd [CATALOG] and scroll down to DIAGNOSTIC ON. Press ENTER twice to select it. Press STAT. Cursor over to the CALC menu, then select 4:LINREG(ax+b). Press VARS. Cursor ...
... Press Graph. The scatter plot will appear. Describe the shape of the graph. c) Perform a linear regression analysis. Press 2nd [CATALOG] and scroll down to DIAGNOSTIC ON. Press ENTER twice to select it. Press STAT. Cursor over to the CALC menu, then select 4:LINREG(ax+b). Press VARS. Cursor ...
Common Core Learning Standards
... Understand that patterns of association can also be seen in bivariate categorical data by displaying frequencies and relative frequencies in a two-way table. Construct and interpret a two-way table summarizing data on two categorical variables collected from the same subjects. Use relative frequenci ...
... Understand that patterns of association can also be seen in bivariate categorical data by displaying frequencies and relative frequencies in a two-way table. Construct and interpret a two-way table summarizing data on two categorical variables collected from the same subjects. Use relative frequenci ...
Chapter 13
... Multicollinearity induces variance inflation in the estimation of the regression model coefficients. This results in wider confidence intervals for the true coefficients b1, b2, …, bk and makes the t statistic less reliable. The separate contribution of each predictor in “explaining” the response va ...
... Multicollinearity induces variance inflation in the estimation of the regression model coefficients. This results in wider confidence intervals for the true coefficients b1, b2, …, bk and makes the t statistic less reliable. The separate contribution of each predictor in “explaining” the response va ...
Logistic Regression
... In a sigma-restricted model, the categories can be assigned any values corresponding to group membership to facilitate interpretation of the regression coefficient associated with the single categorical predictor variable. The values on the resulting predictor variable will represent a quantitative ...
... In a sigma-restricted model, the categories can be assigned any values corresponding to group membership to facilitate interpretation of the regression coefficient associated with the single categorical predictor variable. The values on the resulting predictor variable will represent a quantitative ...
Coefficient of determination
In statistics, the coefficient of determination, denoted R2 or r2 and pronounced R squared, is a number that indicates how well data fit a statistical model – sometimes simply a line or a curve. An R2 of 1 indicates that the regression line perfectly fits the data, while an R2 of 0 indicates that the line does not fit the data at all. This latter can be because the data is utterly non-linear, or because it is random.It is a statistic used in the context of statistical models whose main purpose is either the prediction of future outcomes or the testing of hypotheses, on the basis of other related information. It provides a measure of how well observed outcomes are replicated by the model, as the proportion of total variation of outcomes explained by the model (pp. 187, 287).There are several definitions of R2 that are only sometimes equivalent. One class of such cases includes that of simple linear regression where r2 is used instead of R2. In this case, if an intercept is included, then r2 is simply the square of the sample correlation coefficient (i.e., r) between the outcomes and their predicted values. If additional explanators are included, R2 is the square of the coefficient of multiple correlation. In both such cases, the coefficient of determination ranges from 0 to 1.Important cases where the computational definition of R2 can yield negative values, depending on the definition used, arise where the predictions that are being compared to the corresponding outcomes have not been derived from a model-fitting procedure using those data, and where linear regression is conducted without including an intercept. Additionally, negative values of R2 may occur when fitting non-linear functions to data. In cases where negative values arise, the mean of the data provides a better fit to the outcomes than do the fitted function values, according to this particular criterion.