Regression Analysis
It is a collection of statistical techniques to understand the relationship among several independent variables and one or more dependent random variables.
Inferential Analysis
It is collection of mathematical techniques to make prediction about unseen large data sets on the basis of a study of available small samples of that data.
Regression analysis is a specific way of performing inferential analysis.
of, pertaining to, or determined by regression analysis: regression curve; regression equation. dictionary.com
how can regression model approach be useful in lean construction concept in the mass production of houses
A t-test is a inferential statistic. Other inferential statistics are confidence interval, margin of error, and ANOVA. An inferential statistic infers something about a population. A descriptive statistic describes a population. Descriptive statistics include percentages, means, variance, and regression.
y=a+bx+e sample regression model differentiate between y bar and E(Y)?
The assumptions of Probit analysis are the assumption of normality and the assumption for linear regression.
Before undertaking regression analysis, one must decide on which variables will be analysed. Regression analysis is predicting a variable from a number of other variables.
of, pertaining to, or determined by regression analysis: regression curve; regression equation. dictionary.com
In general in Descriptive Statistics we use tools like central tendency, dispersion, skew, kurtosis to summarize a given set of data. But inferential statistics is much boarder than it. In inferential l statistics we use tools like chi square test, ANOVA, ACOVA, Correlation, Regression, Factor Analysis etc to predict the behavior based on the sample data.
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how can regression model approach be useful in lean construction concept in the mass production of houses
Inferential statistics is the practice of sampling large sets of data (usually at random) to gain information about the population as a whole. Sampling is used because measuring everything in the population can consume too many resources (time, money, etc.) I suggest looking at these topics for an intro into inferential statistics: 1) Sampling (random, stratified, etc) 2) Mean, variance/standard deviation, median, and mode 3) Data distributions 4) Confidence intervals 5) T-tests 6) Analysis of variance 7) Trend analysis (regression) 8) Association analysis ... and many more!
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Regression analysis is a statistical technique to measure the degree of linear agreement in variations between two or more variables.
A t-test is a inferential statistic. Other inferential statistics are confidence interval, margin of error, and ANOVA. An inferential statistic infers something about a population. A descriptive statistic describes a population. Descriptive statistics include percentages, means, variance, and regression.
Howard E. Doran has written: 'Applied regression analysis in econometrics' -- subject(s): Econometrics, Regression analysis
Peihua Qiu has written: 'Image processing and jump regression analysis' -- subject(s): Regression analysis, Image processing
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