![]() ![]() Negative Linear Relationship: When the independent variable increases, the dependent variable decreases.Positive Linear Relationship: When the independent variable increases, the dependent variable increases too.There are basically two types of linear relationships as well. Linear relationship means the change in an independent variable(s) causes a change in the dependent variable. This means these are the variables using which response variables can be predicted. Independent Variable (aka explanatory/predictor variable): Is/are the variable(s) on which response variable is depend.Dependent Variable (aka response/outcome variable): This is the variable of your interest and wanted to predict based on the Independent variable(s).Linear regression is a statistical technique that examines the linear relationship between a dependent variable and one or more independent variables. #2 – Regression Analysis Using Scatterplot with Trendline in Excel.#1 – Regression Tool Using Analysis ToolPak in Excel.How to Perform Linear Regression in Excel?.Explanation of Regression Mathematically.QI Macros also performs Multiple Regression Analysis and Binary Logistic Regression Analysis. This provides you with information on how the confidence level can impact your results, depending on where alpha is set. The 95% and 99% Confidence Levels reference when your alpha value is set at. NOTE: The straight lines found in your first chart (Salt concentration) represent the Upper and Lower Prediction Intervals, while the more curved lines are the Upper and Lower Confidence IntervalsĬonfidence Intervals provide a view into the uncertainty when estimating the mean, while Prediction Intervals account for variation in the Y values around the mean. In addition to the Summary Output above, QI Macros also calculates Residuals and Probability Data and creates scatter plots in Excel for you: Residuals Output, Probability Output and Charts For example, if the % of paved roadway = 1% the Salt concentration could be estimated as 17.547* (1%) +2.6765 = 20.2235 mg/l. Using the equation, y = Salt concentration = 2.677 + 17.547*(% paved roadway area), you could predict the salt concentration based on the percent of paved roadway. Use the Equation for Prediction and Estimation In other words, there is a relation between the two variables. ![]() Since the p value ( 0 < 0.05), we "Reject the Null Hypothesis" that the two variables are unrelated. 951 means that 95.1% of the variation in salt concentration can be explained by roadway area. Some statistics references recommend using the Adjusted R Square value. Evaluate the R Square value (0.951)Īnalysis: If R Square is greater than 0.80, as it is in this case, there is a good fit to the data. ![]() NOTE: If the first cell of your y values column is blank, that column of data will be omitted from your Regression output.
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