# How do you write the results and discussion of a dissertation?

## How do you write the results and discussion of a dissertation?

Table of Contents

When writing a dissertation or thesis, the results and discussion sections can be both the most interesting as well as the most challenging sections to write….Discussion SectionInterpret and explain your results.Answer your research question.Justify your approach.Critically evaluate your study.

## How do you report descriptive statistics in a dissertation?

The following are some key points for writing descriptive results:Add a table of the raw data in the appendix.Include a table with the appropriate descriptive statistics e.g. the mean, mode, median, and standard deviation. Identify the level or data. Include a graph.

## What do you report in descriptive statistics?

Reporting Descriptive Statistics: When reporting descriptive statistic from a variable you should, at a minimum, report a measure of central tendency and a measure of variability. In most cases, this includes the mean and reporting the standard deviation (see below).

## How do you read a descriptive statistics table?

Step 1: Describe the size of your sample. Use N to know how many observations are in your sample. Step 2: Describe the center of your data. Step 3: Describe the spread of your data. Step 4: Assess the shape and spread of your data distribution. Compare data from different groups.

## How do you report the results of descriptive statistics?

In reporting the results of statistical tests, report the descriptive statistics, such as means and standard deviations, as well as the test statistic, degrees of freedom, obtained value of the test, and the probability of the result occurring by chance (p value).

## What are the four types of descriptive statistics?

There are four major types of descriptive statistics:Measures of Frequency: * Count, Percent, Frequency. Measures of Central Tendency. * Mean, Median, and Mode. Measures of Dispersion or Variation. * Range, Variance, Standard Deviation. Measures of Position. * Percentile Ranks, Quartile Ranks.

## How do you interpret skewness in descriptive statistics?

The rule of thumb seems to be:If the skewness is between -0.5 and 0.5, the data are fairly symmetrical.If the skewness is between -1 and – 0.5 or between 0.5 and 1, the data are moderately skewed.If the skewness is less than -1 or greater than 1, the data are highly skewed.

## What is positive skewness?

Positive Skewness means when the tail on the right side of the distribution is longer or fatter. The mean and median will be greater than the mode. Negative Skewness is when the tail of the left side of the distribution is longer or fatter than the tail on the right side. The mean and median will be less than the mode.

## How do you explain normal distribution?

The normal distribution is a probability function that describes how the values of a variable are distributed. It is a symmetric distribution where most of the observations cluster around the central peak and the probabilities for values further away from the mean taper off equally in both directions.

## Why is skewness important in statistics?

The primary reason skew is important is that analysis based on normal distributions incorrectly estimates expected returns and risk. Knowing that the market has a 70% probability of going up and a 30% probability of going down may appear helpful if you rely on normal distributions.

## How do you interpret a positively skewed distribution?

Interpreting. If skewness is positive, the data are positively skewed or skewed right, meaning that the right tail of the distribution is longer than the left. If skewness is negative, the data are negatively skewed or skewed left, meaning that the left tail is longer.

## What is positive and negative skewness?

Explaining Skewness These taperings are known as “tails.” Negative skew refers to a longer or fatter tail on the left side of the distribution, while positive skew refers to a longer or fatter tail on the right. The mean of positively skewed data will be greater than the median.

## What causes positive skewness?

So if the data set’s lower bounds are extremely low relative to the rest of the data, this will cause the data to skew right. Another cause of skewness is start-up effects. For example, if a procedure initially has a lot of successes during a long start-up period, this could create a positive skew on the data.

## Is positive skewness good?

A positive mean with a positive skew is good, while a negative mean with a positive skew is not good. If a data set has a positive skew, but the mean of the returns is negative, it means that overall performance is negative, but the outlier months are positive.

## What does skewness and kurtosis tell us?

Skewness is a measure of symmetry, or more precisely, the lack of symmetry. Kurtosis is a measure of whether the data are heavy-tailed or light-tailed relative to a normal distribution. That is, data sets with high kurtosis tend to have heavy tails, or outliers.

## How do you know if a distribution is skewed?

A distribution is skewed if one of its tails is longer than the other. The first distribution shown has a positive skew. This means that it has a long tail in the positive direction. The distribution below it has a negative skew since it has a long tail in the negative direction.

## What does it mean when a distribution is skewed to the right?

A skewed (non-symmetric) distribution is a distribution in which there is no such mirror-imaging. For skewed distributions, it is quite common to have one tail of the distribution considerably longer or drawn out relative to the other tail. A “skewed right” distribution is one in which the tail is on the right side.

## What does a left skewed histogram mean?

If the histogram is skewed left, the mean is less than the median. This is the case because skewed-left data have a few small values that drive the mean downward but do not affect where the exact middle of the data is (that is, the median).