PGDE

Teacher’s Roles in Education

PDE 703 Revision Questions 4 b) Describe the roles of a professional teacher in detail. (20 marks) (Was asked ONLY in YEAR 2016 Q4b) The roles of a professional teacher are to PDE703 Pg 9: i. Translate scheme of work to a lesson plan and further develop the lesson plan into a lesson note. ii.

A set of 30 people were asked how many coins they had in thier pockets and the following results were obtained : No. Of Coins = 0-4, 5-7, 8-10, 11-12. No. Of people = 6, 8, 8, 8. Find the mean of the coins.

To find the mean number of coins, you’ll need to calculate the weighted average based on the number of people in each group. Here’s how you can do it: Multiply the midpoints of each group by the number of people in that group. Sum up the products from step 1. Divide the sum from step

Explain the following terms: i. Population ii. Statistics iii. Non-probability sampling iv. Sampling technique v. Sample

Explain the following terms: i.Population ii. Statistics iii. Non-probability sampling iv. Sampling technique v. Sample   CORRELATION COEFFICIENT COMPUTATION   [mediator_tech]     State and explain seven (7) assumptions that are made when using the parametric statistics to test a hypothesis       i. Population: Population refers to the entire group of individuals, items,

CORRELATION COEFFICIENT COMPUTATION

Pearson r is employed when the distribution is bivariate, continuous and normal ( continuous and normal. However the scores of the individuals concerned in each variable are approximately so).The Spearman rho is employed when the distribution is bivariate The contingency coefficient and its associates are employed when the data are frequency ranked in order of

QUANTILES OF FRACTILES

Fractiles, quartiles, deciles, and percentiles are terms used in statistics to describe different points that divide a dataset into various segments, helping to understand the distribution of data. Here’s a breakdown of each term: Fractiles: Fractiles are points on a number scale that divide a dataset into specific portions. Quartiles, deciles, and percentiles are examples

Frequency Polygons and frequency curves

Line Graphs: Line graphs use lines to connect data points, usually representing trends or changes over time. They’re great for showing continuous data and how it fluctuates. Bar Graphs: Bar graphs are made up of vertical or horizontal bars to represent data. They’re perfect for comparing categories or groups and showing the magnitude of different

TERMS USED IN FREQUENCY DISTRIBUTIONS

Class Boundaries: Imagine we’re dealing with a set of data, and we want to organize it into groups to better understand it. Class boundaries, also known as true class limits, are numbers that help us define these groups. For instance, in a series of points like 59.5, 62.5, and so on, these points are called

Descriptive Statistics

Four Scales of Measurement: Nominal: Data is categorized into distinct groups with no specific order. Examples include gender, ethnicity, and eye color. Ordinal: Data can be ranked or ordered, but the differences between values are not consistent or meaningful. Example: Education levels (e.g., high school, college, postgraduate). Interval: Data is measured on a scale where

Some Basic Statistical Concepts and Notations

Quantitative Variables take values that very in terms of magnitude. They are easy to measure and compare with one another. These may be scores obtained in a test, weight, height, age, distance, number etc. Qualitative Variables are those that differ in kind. They are only categorized. The differences are usually in kind such as marital

The Meaning of Statistics

(1) Define the term “Statistics” correctly.. (2) Distinguish between statistics and statistic. (3) Discuss the place of statistics in education. (4) Explain the relationship between statistics and probability. (5) Explain clearly some basic statistical concepts and notations.   (1) Statistics refers to the study of collecting, organizing, analyzing, interpreting, and presenting data. It involves methods

Meaning of parametric and non parametric test

  Parametric tests are statistical tests that make specific assumptions about the underlying distribution of the data, such as normality and homogeneity of variance. They typically involve parameters that define the population distribution, like means and variances. Examples include t-tests, ANOVA, and linear regression. Non-parametric tests, on the other hand, do not assume any specific