Histograms are used to show distributions of variables while bar charts are used to compare variables. Histograms plot quantitative data with ranges of the data grouped into bins or intervals while bar charts plot categorical data.

If the histogram is skewed left, the mean is less than the median.

1. If the mean is much larger than the median, the data are generally skewed right; a few values are larger than the rest.
2. If the mean is much smaller than the median, the data are generally skewed left; a few smaller values bring the mean down.

A distribution skewed to the right is said to be positively skewed. This kind of distribution has a large number of occurrences in the lower value cells (left side) and few in the upper value cells (right side). A skewed distribution can result when data is gathered from a system with has a boundary such as zero.

Normal distribution, also known as the Gaussian distribution, is a probability distribution that is symmetric about the mean, showing that data near the mean are more frequent in occurrence than data far from the mean. In graph form, normal distribution will appear as a bell curve.

Insufficient Data can cause a normal distribution to look completely scattered. For example, classroom test results are usually normally distributed. An extreme example: if you choose three random students and plot the results on a graph, you won’t get a normal distribution.

Characteristics of Normal Distribution Here, we see the four characteristics of a normal distribution. Normal distributions are symmetric, unimodal, and asymptotic, and the mean, median, and mode are all equal.

One reason the normal distribution is important is that many psychological and educational variables are distributed approximately normally. This means that many kinds of statistical tests can be derived for normal distributions. Almost all statistical tests discussed in this text assume normal distributions.

Draw a boxplot of your data. If your data comes from a normal distribution, the box will be symmetrical with the mean and median in the center. If the data meets the assumption of normality, there should also be few outliers. A normal probability plot showing data that’s approximately normal.

What’s the key difference between the t- and z-distributions? The standard normal or z-distribution assumes that you know the population standard deviation. The t-distribution is based on the sample standard deviation.

The normal distribution is used when the population distribution of data is assumed normal. It is characterized by the mean and the standard deviation of the data. The t statistic is an estimate of the standard error of the mean of the population or how well known is the mean based on the sample size.

You must use the t-distribution table when working problems when the population standard deviation (σ) is not known and the sample size is small (n<30). General Correct Rule: If σ is not known, then using t-distribution is correct. If σ is known, then using the normal distribution is correct.

Sample size is an important consideration for research. Larger sample sizes provide more accurate mean values, identify outliers that could skew the data in a smaller sample and provide a smaller margin of error.

Z Test is the statistical hypothesis which is used in order to determine that whether the two samples means calculated are different in case the standard deviation is available and sample is large whereas the T test is used in order to determine a how averages of different data sets differs from each other in case …

A histogram is a bar graph-like representation of data that buckets a range of outcomes into columns along the x-axis. The y-axis represents the number count or percentage of occurrences in the data for each column and can be used to visualize data distributions.

To make a histogram, follow these steps:

1. On the vertical axis, place frequencies. Label this axis “Frequency”.
2. On the horizontal axis, place the lower value of each interval.
3. Draw a bar extending from the lower value of each interval to the lower value of the next interval.

How to set the bin size of a Matplotlib histogram in Python

1. data = np. random. normal(50, 10, size = 10000) Creating random data.
2. ax = plt. hist(data)
3. bins_list = [-10, 20, 40, 50, 60, 80, 110] specify bin start and end points.
4. ax = plt. hist(data, bins = bins_list)

Python bin() The bin() method converts and returns the binary equivalent string of a given integer. If the parameter isn’t an integer, it has to implement __index__() method to return an integer. The syntax of bin() method is: bin(num)

The following Python function can be used to create bins.

1. def create_bins(lower_bound, width, quantity): “”” create_bins returns an equal-width (distance) partitioning.
2. bins = create_bins(lower_bound=10, width=10, quantity=5) bins.

The term bank identification number (BIN) refers to the initial set of four to six numbers that appear on a payment card. This set of numbers identifies the institution that issues the card and is key in the process of matching transactions to the issuer of the charge card.

The pandas documentation describes qcut as a “Quantile-based discretization function.” This basically means that qcut tries to divide up the underlying data into equal sized bins. The function defines the bins using percentiles based on the distribution of the data, not the actual numeric edges of the bins.

Steps to plot a histogram in Python using Matplotlib

1. Step 1: Install the Matplotlib package.
2. Step 2: Collect the data for the histogram.
3. Step 3: Determine the number of bins.
4. Step 4: Plot the histogram in Python using matplotlib.

How to Plot a Histogram in Python

1. In [5]: df. head(1) Out[5]:
2. In [6]: import matplotlib.pyplot as plt. Lets just pick one column from dataframe and plot using matplotlib.
3. In [22]: df[‘Apps’]. plot(kind=’hist’)
4. In [34]: fig, ax = plt. subplots(figsize=(5,3)) plt.
5. In [44]: sns. distplot(df[‘Apps’],kde=False,bins=5) plt.

Histograms represent the data distribution by forming bins along the range of the data and then drawing bars to show the number of observations that fall in each bin. Seaborn comes with some datasets and we have used few datasets in our previous chapters.

The plotting library Seaborn has built-in function to make histogram. The Seaborn function to make histogram is “distplot” for distribution plot.

We start with the typical imports:

1. import matplotlib.pyplot as plt plt. style.
2. # Create some data rng = np. RandomState(0) x = np.
3. In [3]: # Plot the data with Matplotlib defaults plt.
4. import seaborn as sns sns. set()
5. In [5]: # same plotting code as above!
6. data = np.
7. for col in ‘xy’: sns.
8. sns.

Changing the Size of Seaborn Plots First, we need to install the Python packages needed. Second, we are going to create a couple of different plots (e.g., a scatter plot, a histogram, a violin plot).

Change Matplotlib Plot Size

1. Set the figsize in figure() Methods to Set Matplotlib Plot Size.
2. Set rcParams to Change Matplotlib Plot Size.
3. set_figheight() Along With set_figwidth() to Set Matplotlib Plot Size.
4. set_size_inches() Method to Change the Matplotlib Figure Size.
5. Change Figure Format in Matplotlib.

We may want to set the size of a figure to a certain size. You may want to make the figure wider in size, taller in height, etc. We can do this with matplotlib using the figsize attribute. The figsize attribute allows us to specify the width and height of a figure in unit inches.

The word ‘plot’ is an arbitrary term used to describe a land division carved out for property development. An Acre consist of 6 plots each measuring 6 x 120ft. In Lagos State, the standard size of a plot is 60 x 120ft ( 18m x 36m ie 648sqm), while in some other cities of the country, plots are measured in 50 x100ft.

The default value is [8.0, 6.0] which can be changed of course.

matplotlib is the most widely used scientific plotting library in Python.

1. import matplotlib.pyplot as plt.
2. time = [0, 1, 2, 3] position = [0, 100, 200, 300] plt. plot(time, position) plt.
3. import pandas as pd data = pd.
4. data.
5. plt.
6. years = data.
7. # Select two countries’ worth of data.
8. plt.