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**Statistics Assignment Help**

This branch of mathematics is mainly concerned with coming up with techniques to facilitate people in presenting, interpreting and analyzing empirical information. Two Statistical concepts that serve as a basis for this discipline are uncertainty and variation respectively. Almost every situation encountered by a person, whether scientific or naturalistic, is marked by uncertainty. There are two explanations for this:

The consequences or results of a certain behavior cannot be predetermined (e.g. not knowing what'll happen tomorrow).

The outcomes have been determined but the person of interest isn't aware of it (e.g. not knowing what score one has achieved on a certain test).

Variation, on the other hand is bound to surface when measurements are repeated. Statistics tutors maximize their efforts in eliminating any confounding variables and provide Statistics Assignment Help which would result in a different measurement than that from last time.

DEFINING CHARACTERISTICS:

- It is a collection of facts.
- These facts are expressed numerically.
- Statistical collection is done in an extremely systematic and organized way.
- One should be able to draw a comparison with the acquired statistical knowledge.

**STATISTICAL BRANCHES WE PROVIDE HELP FOR:**

Statistics can be divided into two distinct branches:

Descriptive Statistics: This branch is mostly concerned with the collection, description and presentation of statistical data. Preparing annual reports in businesses can be taken as an example.

Inferential Statistics: One analyzes estimates and draws successful conclusion in this field from the information that is obtained via checking upon the estimate's reliability. This is mostly done by sampling and conducting tests on a population.

**BENEFITS OF STATISTICS IN YOUR STUDY:**

It helps in understanding natural phenomena better and also guides one to investigate its roots thoroughly regardless of the field.One can derive data that is relevant and quantitative in nature.For making the data comprehensible, tables, diagrams, charts etc. are used.

APPLICATION IN OTHER FIELDS:

Statistical knowledge is utilized in some of the following disciplines:

**Environmental statistics** :

It entails applying statistical methods to the study of elements of the environment such as air, water, earth etc.

**Statistical physics:**

The theory of probability which as mentioned above is applied in this field to extract solutions to its problems.

**Statistical thermodynamics: **

It is the microscopic study of the thermodynamics functioning and it utilizes probability to interpret quantities like heat, entropy and work etc. molecularly.

Other fields include econometrics, business analytics, psychometric, geostatistics, machine learning and quantitative psychology, quality control etc.

**LIMITATIONS OF STATISTICS****:**

Statistical knowledge is applicable to studies involving aggregates or groups only.

Statistics can be misleading in cases which lack precision in steps such as collection, analysis and interpretation. Trained and expert individuals are preferred for such positions so as to not skew the results obtained from a specific study.

Results obtained from a single source cannot be analyzed or evaluated in statistical terms.

They are recommended for explaining results derived from quantitative data and are not applicable to data that is heterogeneous in nature.

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Statistics is a known as a distinctive branch of mathematics while not exactly mathematics. It deals with the collection and analysis of data and grouping and analyzing the data that includes numerical forms and conversion of results. It refers the study of organizing data and determining results using different techniques and tools. It generally includes the study of interpretation, derivation, analysis and collection of required information.

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## Data Analysis Project: Descriptive Statistics

**Question:**

Describe about the current era, data analysis is the key element for analysis of any statistical problem?

## Answer:

**Introduction:**

In current era, data analysis is the key element for analysis of any statistical problem. Thus the analysis has to do on the data regarding the time taken to travel to school by Australian students. According to given data it will be look out what average time will required to student to reach the school. Along with that some descriptive statistic for this time variable will be look out. Time to travel to reach the school is given in minutes. Under this study of data for the given variable, we have to see the histogram for this variable. We have to use excel or SPSS for analysis of given data for the variable time required to reach the school for Australian students. Let us see this statistical analysis in detail given below:

**Data Analysis:**

In the data analysis part, we have to see some descriptive statistics and histogram for the variable time required for Australian students to reach the school. Descriptive statistics is nothing but the study of mean, mode, median, maximum, minimum, skewness, kurtosis etc. We know that the histogram represents the frequency distribution. In this part, we have to see the histogram for the variable time required for Australian students to reach the school. Let us see the descriptive statistics for the variable time required for Australian students to reach the school. The descriptive statistics for this variable is given below:

| ||||||

N |
Minimum |
Sum |
Mean |
Std. Deviation |
Variance | |

Time_to_school |
125 |
1.00 |
2470.00 |
19.7600 |
22.97629 |
527.910 |

Valid N (listwise) |
125 |

Here, we get the minimum time in minutes for Australian student to reach his school is given as 1 minute. The average time required for reaching to school is given as 19.76 minutes for Australian students. The standard deviation is given as 22.98 minutes. We have to see some other descriptive statistics for the variable time required for Australian students to reach the school which is given in the following table:

| ||||||||

N |
Range |
Maximum |
Mean |
Skewness |
Kurtosis | |||

Statistic |
Statistic |
Statistic |
Std. Error |
Statistic |
Std. Error |
Statistic |
Std. Error | |

Time_to_school |
125 |
149.00 |
150.00 |
2.05506 |
3.148 |
.217 |
12.618 |
.430 |

Valid N (listwise) |
125 |

There are total 125 students are participated in this survey. Data is collected for the time required to reach the school. The range for time required to reach the school is given as 149 minutes. The maximum time required for reaching to school is given as 150 minutes. Coefficient of skewness describes the skew of the distribution for the variable under study. Here, we get coefficient of skewness as 3.148; this means, coefficient of skewness is greater than zero and it is a positive coefficient. So, we interpret that the given variable time required to reach the school have asymmetrical distribution with a long tail to the right. Here, we get the coefficient of Kurtosis as 12.618, this means, the study variable have a distribution more peaked than a Gaussian or normal distribution.

Histogram shows the exact nature of the frequency distribution of the study variable. The histogram for the variable time to reach the school for Australian students is given below:

From above histogram we conclude that the variable time taken by Australian student to reach the school have an asymmetrical distribution with a long tail to the right.

**Interpretations:**

1) Average time for Australian students to reach the school is found as 19.76 minutes.

2) Minimum time required for Australian student to reach the school is 1 minutes.

3) Maximum time required to reach the school is 150 minutes.

4) We conclude that the variable time taken by Australian student to reach the school have asymmetrical distribution with a long tail to the right.

5) We interpret that the variable time required to reach the school have distribution more peaked than a normal distribution.

**References:**

1) Robert V. Hogg, Allen T. Craig, Joseph W. McKean, An Introduction to Mathematical Statistics, 6th ed., Prentice Hall, 2004.

2) George Casella, Roger L. Berger, Statistical Inference, 2nd ed., Duxbury Press, 2001.

3) David R. Cox, D. V. Hinkley, Theoretical Statistics, Chapman & Hall/CRC, 1979.

4) Peter J. Bickel, Kjell A. Doksum, Mathematical Statistics, Volume 1, Basic Ideas and Selected Topics, 2rd ed. Prentice Hall, 2001.

5) T. S. Ferguson, Mathematical Statistics: A Decision Theoretic Approach, Academic Press, Inc., New York, 1967