Class 8 Measurement 2020 Sp
Measurement involves assigning values to outcomes.
Examples of measurement include percentages, numerical values like 9.3 mg/dl, and categorical distinctions like high vs. low social class.
Nominal level assigns names or categories.
Ordinal level assigns categories with order.
Interval level includes equal intervals between categories.
Ratio level has equal intervals and an absolute zero point.
Descriptive statistics describe data characteristics.
Inferential statistics draw conclusions from data for broader applications.
Power calculations, such as determining sample size, are essential in statistical analysis.
Descriptive statistics cover data distribution, variance, central tendency measures (mean, median, mode), and variability around the mean.
Inferential statistics involve confidence intervals, types of errors (Type I and Type II), and statistical tests like T-tests, ANOVAs, correlations, regressions, and relative risk analysis.
Confidence intervals indicate a range of values that likely includes the true population mean.
They are expressed with a confidence level (e.g., 95%) and upper and lower limits.
Type I error involves falsely rejecting the null hypothesis.
Type II error occurs when the null hypothesis is not rejected despite an actual difference.
Power is the probability of avoiding a Type II error.
T tests compare means of two groups and determine statistical significance through p-values.
A significance level (α) is typically set at 0.05, with p ≤ 0.05 indicating a significant difference.
Analysis of variance (ANOVA) compares means of three or more groups, offering a more robust alternative to multiple t-tests.
Various ANOVA tests exist based on data types and shapes.
Correlations measure relationships between variables, with the coefficient (r) ranging from -1.0 to +1.0.
The strength and direction of the relationship are indicated by the correlation coefficient.
Regression models the relationship between dependent and independent variables.
R2 value describes how well the regression line fits the data, ranging from 0.0 to 1.0.
Measurement involves assigning values to outcomes.
Examples of measurement include percentages, numerical values like 9.3 mg/dl, and categorical distinctions like high vs. low social class.
Nominal level assigns names or categories.
Ordinal level assigns categories with order.
Interval level includes equal intervals between categories.
Ratio level has equal intervals and an absolute zero point.
Descriptive statistics describe data characteristics.
Inferential statistics draw conclusions from data for broader applications.
Power calculations, such as determining sample size, are essential in statistical analysis.
Descriptive statistics cover data distribution, variance, central tendency measures (mean, median, mode), and variability around the mean.
Inferential statistics involve confidence intervals, types of errors (Type I and Type II), and statistical tests like T-tests, ANOVAs, correlations, regressions, and relative risk analysis.
Confidence intervals indicate a range of values that likely includes the true population mean.
They are expressed with a confidence level (e.g., 95%) and upper and lower limits.
Type I error involves falsely rejecting the null hypothesis.
Type II error occurs when the null hypothesis is not rejected despite an actual difference.
Power is the probability of avoiding a Type II error.
T tests compare means of two groups and determine statistical significance through p-values.
A significance level (α) is typically set at 0.05, with p ≤ 0.05 indicating a significant difference.
Analysis of variance (ANOVA) compares means of three or more groups, offering a more robust alternative to multiple t-tests.
Various ANOVA tests exist based on data types and shapes.
Correlations measure relationships between variables, with the coefficient (r) ranging from -1.0 to +1.0.
The strength and direction of the relationship are indicated by the correlation coefficient.
Regression models the relationship between dependent and independent variables.
R2 value describes how well the regression line fits the data, ranging from 0.0 to 1.0.
