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• Descriptive Statistics Assignment Help

## Descriptive Statistics Assignment Help

Descriptive statistics is a field of statistics that describes or explains a set of data. They are used for two purposes – to show potential relationships between variables and to provide an overall summary of variables in a data set. Initially, when we are given a set of data, we first try to understand it instead of just rushing ad applying fancy algorithms. Descriptive statistics help us summarize and organize data so that we can easily understand it.

At Statistics Assignment Experts, we provide descriptive statistics homework help to assist students who are struggling with projects on this subject. We offer assignment writing for those who would like their papers written from scratch and assignment rewriting for those who have resits. We also provide editing and proofreading services as well as online tutoring on various descriptive statistics topics. You can get any kind of assistance from our descriptive statistics assignment help platform. All you need to do is communicate your need to us and we will make sure that you are receiving the best help with descriptive statistics assignments. ### Types Of Descriptive Analysis

Descriptive analysis can be divided into two major categories:

• Measures of central tendency
• Measures of dispersion

#### Measure Of Central Tendency

Central tendency means that there is a number that in some way summarizes the entire data set. There are three main methods to show the central tendency. These include:

• Mean: This is the average of a given data set. It is calculated by adding the data together and then dividing the total sum by the number of data.

For example, let’s say you have five people whose weight is 60, 70, 80, 90, 100 kg. To get the mean, just add the numbers, and divide by 5. So, it will be 60+70+80+90+100=400. Then 400/5=80. In this case, the average weight will be 80 kg.

To use mean as a measure of central tendency your data must be numerical. You cannot use mean when your dataset it made of nominal data.

• Mode: This is the number that occurs most often in a dataset.

For example, consider a dataset with the height (in inches) of ten people: 56, 56, 56, 57, 57, 58, 59, 59, 60, 61. As you can see, 56 is the most common value, hence the mode of this set of data would be 56. The mode has one benefit over the mean and median – it can be used for both numerical and nominal datasets.

• Median: The median is simply the number in the middle of a dataset. To calculate the median, you need to first list the numbers in an ascending or descending order and then locate the number in the middle.

For example, the median of the following set of data would be 27 because it is the value at the center.

22, 23, 25, 25, 27, 28, 29, 32, 33

But this example has an odd set of values (9 numbers). What happens when you have an even dataset? Easy! Just locate the two numbers in the middle and divide them by two.

For example, the following data set has 10 values.

22, 23, 25, 25, 27, 28, 29, 32, 33, 34

The median will therefore be (27+28) then divide by 2, which will be 27.5

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#### Measures Of Dispersion

While central tendency shows us important information about a dataset, it doesn’t really reveal everything we need to know about the dataset. For instance, it doesn’t show us the extent to which individual values differ in a dataset. According to our descriptive statistics assignment help experts, measures of dispersion give a lot more information about a dataset. They show us the averages of the values in a dataset and allow us to learn and interpret them more comprehensively. Dispersion describes how data values are spread within a given set of data. There are two common measures of dispersion in statistics; the range and standard deviation.

• Range: The range can be defined as the difference between the largest value and the smallest value in a given set of data. It shows the amount of variation there is from the average. A high range shows that the values are very far from the mean and a low range shows the opposite. Here is an example:

Suppose you have two sets of data, one with retirement age and the other with height (in inches) of a given population.

Data set 1 (Age): 57, 59, 61, 63, 65

Data set 2 (Height): 45, 55, 65, 75, 85

Here is how to calculate the range of both data sets:

Data set 1: 65 – 57 = 8

Data set 2: 85 – 45 = 40

As you can see, the values in data set 1 are closer to the mean than those in data set 2. The major downside of the range as a measure of dispersion is that it only gives us information about the maximum and minimum of the set of data. It doesn’t give any information about the values that are in between. To learn more about the range as a measure of dispersion, contact our descriptive statistics homework helpers.

• The standard deviation: This measure of dispersion also provides information about the variation of a data set from the mean. But unlike the range, standard deviation digs a little deeper and shows us how each and every value of the data set deviates from the mean. However, just like in the range, a high standard deviation shows that the data points are close to the mean and a low standard deviation means the opposite. For more information and examples on standard deviation, connect with our descriptive statistics assignment help experts.

### Topics Covered By Our Descriptive Statistics Assignment Helpers

Looking for descriptive statistics homework help but worried that your topic might be too difficult to handle? You need not worry! Our experts can tackle any kind of descriptive statistics assignment topic irrespective of its intricacy. They have handled many topics before including:

• Average
• Median
• Measures of location
• Range
• Standard deviation
• Percentile
• Variance
• Absolute deviation
• Density estimates
• Generalized bootstrap function
• Linear and rank correlation
• Tabulation and diagrammatical representation of statistical data
• Cumulative distribution
• Frequency distribution
• Frequency curve and ogive
• Frequency polygon
• Histogram
• Univariate quantitative data
• Fitting of binomial
• Bivariate quantitative data
• Poisson and normal distribution
• Regression analysis
• Scatter diagram
• Correlation index
• Multiple correlation
• Partial correlation
• Hypergeometric distribution
• Bivariate normal distribution
• Chebychev’s inequality

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