Inferential statistics and multiple regressions (questions 3a-c) A consultancy company was asked to investigate the impact of total quality management (TQM) practices on innovation performance of manufacturing organisations located in the region of East England. For this reason, the researchers wanted to conduct a survey questionnaire to examine whether the application of specific TQM practices enabled organisations to build their competence and competitiveness through innovation. The CEO of each organisation was asked to fill a questionnaire consisting of 36 questions divided in six different sections. The first five sections of the questionnaire focus on TQM practices. A total of 123 manufacturing organisations were contacted and 110 of them replied. The dependent variable is Innovation performance and it is measured by Innovation Sales Rates (ISR) as the percentage of sales of “new products” on the total sales of all products. The independent variables are the following specific TQM practices: LEADERSHIP STRATEGIC PLANNING CUSTOMER FOCUS PROCESS MANAGEMENT PEOPLE MANAGEMENT For each practice, a specific measurement scale was created to measure the answers from the respective questionnaire section. Define and comment on the statistical significance, relative strength and direction of the relationships between the dependent variable and each independent variables of the table above.
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.
A consultancy company was asked to investigate the impact of total quality management (TQM) practices on innovation performance of manufacturing organisations located in the region of East England. For this reason, the researchers wanted to conduct a survey questionnaire to examine whether the application of specific TQM practices enabled organisations to build their competence and competitiveness through innovation.
The CEO of each organisation was asked to fill a questionnaire consisting of 36 questions divided in six different sections. The first five sections of the questionnaire focus on TQM practices. A total of 123 manufacturing organisations were contacted and 110 of them replied.
The dependent variable is Innovation performance and it is measured by Innovation Sales Rates (ISR) as the percentage of sales of “new products” on the total sales of all products.
The independent variables are the following specific TQM practices:
LEADERSHIP
STRATEGIC PLANNING
CUSTOMER FOCUS
PROCESS MANAGEMENT
PEOPLE MANAGEMENT
For each practice, a specific measurement scale was created to measure the answers from the respective questionnaire section.
Define and comment on the statistical significance, relative strength and direction of the relationships between the dependent variable and each independent variables of the table above.
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