RANK Created Variables Regression Source Variable Function New Variable Label X1 Fractional Rank Percent PERO01 Fractional Rank Percent of X1 Variables EnteredRemoved Fractional Rank Percent PER002 Fraçtional Rank Percent of X2 Variables Entered Variables Removed X3 Y1 Fractional Rank Percent PER003 Fractional Rank Percent of X3 Model Method X3, X1, x2 Enter Fractional Rank Percent PY1 Fractional Rank Percent of Y1 a. Dependent Variable: Y1 a. Mean rank of tied values is used for ties. b. All requested variables entered. b. Ranks are in ascending order. Model Summary Adjusted R Square Std. Error of Explore R Square the Estimate 3.97213 Model R 478 229 215 a. Predictors: (Constant), X3, X1, X2 Case Processing Summary Čases ANOVA Valid Missing Total Sum of Squares Percent N Percent Percent Model of Mean Square F Si. Regression 775.883 3 258.628 16.392 .000 Pump Presure 170 100.0% 0.0% 170 100.0% Residual 2619.119 166 15.778 Density 170 100.0% 0.0% 170 100.0% Total 3395.002 169 Viscosity a. Dependent Variable: Y1 b. Predictors: (Constant), X3, X1, X2 170 100.0% 0.0% 170 100.0% Descriptives Coefficients Statistic Std. Error Standardized Unstandardized Coefficients Coefficients Pump Presure Mean 18.3735 07043 Model B Std. Error Beta Sig. 95% Confidence Interval for Mean Lower Bound 18.2345 (Constant) -177.749 60.845 -2.921 .004 Upper Bound 18.5126 X1 -870 352 178 -2.472 .014 X2 111.901 34.646 273 3.230 .001 5% Trimmed Mean 18.3660 X3 2.278 .820 238 2.780 06 Median 18.0000 a. Dependent Variable: Y1 Variance 843 Std. Deviation .91836 Minimum 16.00 Maximum Descriptives 21.00 Range 5.00 Interquartile Range 1.00 Descriptive Statistics Skewness 536 .186 N Range Minimum Maximum Mean Std. Deviation Variance Kurtosis 449 370 Pump Presure 170 5.00 16.00 21.00 18.3735 91836 843 Density Mean 1.7502 00084 Density 170 .06 1.72 1.78 1.7502 .01093 .000 95% Confidence Interval Lower Bound 1.7486 Viscosity 170 2.45 2.68 5.13 3.8696 46779 219 for Mean Upper Bound 1.7519 30# (%) 170 27.50 3.20 30.70 10.9341 4.48205 20.089 5% Trimmed Mean 1.7502 Valid N (listwise) 170 Median 1.7500 Variance 000 Std. Deviation .01093 PPlot Minimum 1.72 Maximum 1.78 Model Description Range .06 Model Name MOD_1 Interquartile Range 02 Series or Sequence Pump Presure Skewness 008 .186 2 Density Kurtosis 594 370 3 Viscosity Viscosity Mean 3.8696 .03588 Transformation None 95% Confidence Interval Lower Bound 3.7988 Non-Seasonal Differencing for Mean Upper Bound 3.9405 Seasonal Differencing 5% Trimmed Mean 3.8766 Length of Seasonal Period No periodicity Median 3.8400 Standardization Not applied Variance 219 Distribution Type Normal Std. Deviation 46779 Location estimated Minimum 2.68 Scale estimated Maximum 5.13 Fractional Rank Estimation Method Blom's Range 2.45 Rank Assigned to Ties Mean rank of tied values Interquartile Range .52 Applying the model specifications from MOD_1 Skewness 042 .186 Kurtosis 344 370 Case Processing Summary Pump Presure Tests of Normality Density Viscosity Kolmogorov-Smirnov Shapiro-Wilk Series or Sequence Length 170 170 170 Statistic df Sig. Statistic df Sig. Number of Missing User-Missing Pump Presure 299 170 .000 858 170 .000 Values in the Plot System-Missing Density 185 170 .000 913 170 .000 The cases are unweighted. Viscosity .114 170 .000 969 170 .001 a. Lilliefors Significance Correction Estimated Distribution Parameters Pump Presure Density Viscosity Normal Distribution Location 18.3735 1.7502 3.8696 Scale 91836 01093 46779 The cases are unweighted.
Correlation
Correlation defines a relationship between two independent variables. It tells the degree to which variables move in relation to each other. When two sets of data are related to each other, there is a correlation between them.
Linear Correlation
A correlation is used to determine the relationships between numerical and categorical variables. In other words, it is an indicator of how things are connected to one another. The correlation analysis is the study of how variables are related.
Regression Analysis
Regression analysis is a statistical method in which it estimates the relationship between a dependent variable and one or more independent variable. In simple terms dependent variable is called as outcome variable and independent variable is called as predictors. Regression analysis is one of the methods to find the trends in data. The independent variable used in Regression analysis is named Predictor variable. It offers data of an associated dependent variable regarding a particular outcome.
I need to rectify below issues. Please help me.
1. Model Summery
2. Interpretation about ANOVA (output significant value)
3. How we manually calculate Regression and interpretation (using the formula of Y=βo+βx1+βx2+βx3+e)
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