Nowadays, the knowledge of basic concepts of statistics has become necessary. The reason for this is data. There are many industries that rely on different data for the betterment and growth of the industry.
Data helps industrialists to make better decisions by understanding the market needs and more. That is why statistics is one of the important factors that a statistician must know. Statistics is nothing but the method of collection, analysis, representation, and drawing the relevant conclusions from raw data.
Moreover, there are 4 major statistics elements, parameter, sample, variable, and statistic (singular). These elements are used for getting the desired results (that is information) from the raw data.
Below, we have explained the most basic concepts of statistics that a statistician must know. Scroll down the page to check them one by one along with the useful details.
Top 5 basic concepts of statistics to learn in 2021
We have already mentioned that the knowledge of basic concepts of statistics is extremely important. Therefore, we have categorized them into 5 different sections. These are as follows:
- Central Tendency
- Hypothesis Testing
Let’s check their detailed explanation to get more ideas about these basic concepts of statistics.
It is the likelihood’s measure that is the event that happens in a Random Experiment.
- Intersection: P(A∩B) = P(A)P(B)
- Complement: P(A) + P(A’) = 1
- Union: P(A∪B) = P(A) + P(B) − P(A∩B)
Independent Events: The two events can be independent when the occurrence of an event does not affect the occurrence’s probability of the other. P(A∩B)=P(A)P(B) where P(B|A)=P(B), P(A|B)=P(A), P(A) != 0, and P(B) != 0
Conditional Probability: P(A|B) is the probability measure of one event happening with some relation between one or more events. P(A|B)=[P(A∩B)]/P(B), if the P(B)>0.
Bayes’ Theorem: This represents an event’s probability. It depends on previous knowledge of the condition, and this would be related to the event.
Mutually Exclusive Events: Two events can be mutually exclusive when they both do not happen simultaneously. P(A∪B)=P(A)+P(B) and P(A∩B)=0.
Range: It is the difference between the maximum and minimum values in the given dataset.
Standard Deviation: The standard difference among every data point and the average and the variance’s square root.
Variance: It is defined as the mean squared values’ difference from the mean. Moreover, variance explains how the spread out of a data set is relative to an average.
Standard Error (SE): It is the estimate of the sampling distribution’s standard deviation.
- Central Tendency
Mean: The dataset’s average.
Mode: Mode is the most frequent data value present within the dataset. Or we can say that the data have multiple values that happened the maximum time, then you will have a multimodal distribution.
Median: It is the ordered dataset’s middle value.
Kurtosis: Kurtosis helps in measuring whether the data are light-tailed or heavy-tailed related to a normal distribution.
Skewness: It is the symmetry’s measure.
Linear Regression: The linear approach for modelling the relations between an independent variable and a dependent variable.
A dependent variable is one of the variables that is calculated in a particular scientific investigation.
An independent variable is one of the variables controlled in a specific scientific investigation. These variables are used for testing certain effects over the dependent variable.
Assumptions of Linear Regression
- Multivariate Normality
- Linear Relationship
- No or Little Multicollinearity
- No or Little Autocorrelation
Multiple Linear Regression: The linear approach for modelling the relations among two or multiple independent variables and a dependent variable.
- Hypothesis Testing
Null and Alternative Hypothesis
Null Hypothesis: In simple words, we can say that there is no connection between the two determined phenomena or no relationship between the groups.
Alternative Hypothesis: This is being opposed to the null hypothesis.
It has been seen that there are two different errors that happen in statistical hypothesis testing. The type I error uses for a true null hypothesis’s rejection. And another is the type II error and is used for a false null hypothesis’s non-rejection.
The interpretation of the hypothesis done using:
- Critical Value
- Significance level & rejection region
Let’s wrap it up!!
Statistics concepts’ knowledge is useful for discoveries in science, making predictions, making decisions dependent on data, and more. Therefore, with the help of the above-mentioned basic concepts of statistics, you can easily draw relevant conclusions.
We have mentioned all the major statistical terminologies. Try to learn them daily till your concepts do not get cleared. Moreover, practice these concepts with suitable examples so that your conceptual knowledge improves more and more. Make a schedule to learn these concepts and follow them strictly. Or, if someone of you need someone to help you with your assignments or write my essay online then you can also get the help with the professionals of this industry.