spearman's rho
makeing the correlation spurious
is it necessary to have periodically assess and evaluate policies because it determines whether the policies are still current & relevant to your organisation & to its clients. Whether the policies suits their intended purpose or whether they need to be changed, eliminated, replaced or even updated.
Chi-square is mainly used for a goodness of fit test. This is a test designed to assess how well a set of observations agree with what might be expected from some hypothesised distribution.
measures that are relevant are: (1) the ratio of program expenditures to total expenditures; (2) the ratio of administrative overhead to total expenditures; (3) the ratio of fund-raising expenditures to total expenditures
The verb derating means to assess the value of (some types of property, such as agricultural land) at a lower rate than others for local taxation; operation of a machine at less than its rated maximum power in order to prolong its life.
spearman rhos
Chi-square is a statistic used to assess the degree of the relationship and degree of association between two nominal variables
makeing the correlation spurious
The Pearson correlation coefficient is commonly used to assess the linear relationship between two continuous variables. If the data does not meet the assumptions of normality, the Spearman rank correlation can be utilized as a non-parametric alternative. Both tests provide insights into the strength and direction of the correlation between the variables.
A scatter plot is a graphical technique commonly used to display correlations between two variables. It allows you to visually observe the relationship between the variables and assess the strength and direction of the correlation.
To be valid, an experiment must not include bias, confounding variables, or unreliable measures in order to accurately assess the cause-and-effect relationship between variables.
To determine if the relationship between variables is additive or multiplicative, you need to analyze how changes in one variable affect the other. An additive relationship suggests that a change in one variable results in a constant change in the other, while a multiplicative relationship indicates that the change in one variable affects the other by a proportion or factor. You can often assess this by examining the form of the data or the results of regression analysis. If the interaction between variables can be described using addition, it's additive; if it involves multiplication, it's multiplicative.
yes
Spearman's Rho is used when you want to assess the strength and direction of a monotonic relationship between two variables that may not necessarily be normally distributed. It is particularly useful for ordinal data or when the assumptions of parametric tests, like Pearson's correlation, are violated. Additionally, it is appropriate when dealing with small sample sizes or when the data contains outliers, as it is less sensitive to these issues.
ANOVA (Analysis of Variance) is used for interval and ratio level data because it relies on the assumption that the data is continuous and normally distributed, allowing for meaningful calculations of means and variances. Nominal and ordinal data do not meet these criteria; nominal data consists of categorical variables without a numerical relationship, while ordinal data has a ranked order but does not provide equal intervals between ranks. Consequently, ANOVA is not appropriate for these data types as it cannot accurately assess differences in means or variances.
Correlation is used to assess the strength and direction of a relationship between two variables. It is helpful when you want to determine if and how two variables are related to each other, but it does not imply causation. Correlation analysis is commonly used in research, statistics, and data analysis to understand patterns and associations between variables.
Assess The Relationship Between Motivation Theory And The Practice Of Management