Data Analysis and Freelancing with Microsoft Excel

Master the research process from introduction to conclusion and learn descriptive and inferential statistics using the Excel data analysis toolpak.

Explore the basics of statistics, including descriptive and inferential statistics. Learn population versus sample, census versus sampling, and key notations like mu, x-bar, sigma, and pi.

Distinguish secondary and primary data and classify variables as qualitative or quantitative, including discrete and continuous types, with examples such as gender, age, income, and balance.

Learn the levels of measurement: nominal, ordinal, interval, and ratio, and how they shape data analysis, differences, zero points, and comparisons for variables like temperature, income, and time.

Link levels of measurement to variable types, classify nominal and ordinal as qualitative and interval and ratio as numeric, and apply descriptive and inferential statistics for CGP and age data.

Learn descriptive statistics, build frequency tables and charts (pie, bar, histogram) in Excel, analyze data, and report results in APA format, while exploring freelancing as a data analyst.

Learn to code gender as 0/1 in Excel, build frequency tables, and create bar, pie, and histogram charts using the data analysis toolpak.

Learn to report data findings in a paper format by creating frequency distributions and charts—pie for gender, bar for class frequencies, and a CGP histogram—labeled in Microsoft Word.

Explore central tendency in descriptive statistics and learn how the arithmetic mean represents population and sample averages, with median, mode, geometric and weighted means, using Excel to calculate the mean.

Explore dispersion, including the range, variance, and standard deviation—the latter being the common measure—plus degrees of freedom and standard scores for cross-scale comparisons.

Explore the shape of a distribution by identifying symmetry, positive skewness, negative skewness, and bimodal forms. Learn how skewness indicates direction and how the height reflects the distribution’s characteristics.

The lecture demonstrates calculating central tendency and dispersion in Excel using descriptive statistics, the 95 percent confidence interval, standard deviation, skewness, and interpreting the population mean from the sample mean.

Learn how to report descriptive statistics in Excel outputs, including standard deviation, skewness, and confidence intervals, and interpret mean values and the normal distribution to describe study data.

Explore hypothesis testing as the core of inferential statistics, defining null and alternate hypotheses, selecting a level of significance, and testing with sample results to infer population parameters.

Select a suitable test statistic, such as z, t, or F, based on known or unknown sigma. Compare it to the critical value to decide whether to reject null hypothesis.

Explore one-sample and two-sample hypothesis tests, using sample means to infer population means, with known vs unknown population standard deviation, including z-tests, t-tests, and dependent or independent samples.

Explore two-tailed tests and tail concepts in hypothesis testing, showing how null uses equality, the alternate is not equal, and how right or left tail decisions follow hypothesis direction.

Explore the steps of hypothesis testing with a two-tailed test: define null and alternative hypotheses, set 0.05 significance, choose z or t, apply critical values and p-values to decide.

Formulate null and alternative hypotheses about the population mean, then use a one tailed Z statistic and a significance level with the p-value to decide rejection.

Perform a one-sample z-test in Excel to assess if the population mean equals 2.5, using a known standard deviation and variance for a 15-sample study, with p-values guiding null rejection.

Perform a one-sample t-test in Excel, choose unequal variance, input a hypothesized mean difference of 2.5, and interpret the p-value to decide on the null hypothesis in a two-tailed test.

Learn how to conduct a one-sample z-test for a population mean, interpret two-tailed p-values, and decide on rejecting the null hypothesis with an example involving CGP.

Learn how to conduct an independent two-sample t-test to compare means between two groups, decide equal or unequal variances, and interpret p-values against a significance level.

Explore data analysis in Excel by splitting data into male and female groups, performing an independent two-sample t-test with equal variance, and interpreting means, variance, and p-values.

Report the results of an independent samples t-test comparing male and female CGP, including means, standard deviations, and p-values, showing females higher and rejecting the null hypothesis.

Learn the two-sample z test for comparing means of two independent groups with known population standard deviations, and compare it to the independent sample t-test using null hypotheses and p-values.

Perform data analysis in Microsoft Excel to compare male and female CGP using a two-sample z test with known variances, showing female CGP higher and p-value very low.

Learn to perform a paired t-test to compare pre-test and post-test means from related samples, test hypotheses at 0.05, and decide using t or p-value.

Using Excel's data analysis tools, conduct a paired t-test on pre- and post-test CGP, test the null hypothesis with p values, and interpret significance and post-test improvement.

Analyze the pre- and post-test CGP with a t-test, showing p < 0.001 and a higher post-test mean. Reject the null and note the difference; discuss one-tailed versus two-tailed framing.

Set up a Fiverr profile with a professional image and concise bio, highlight data analysis and Microsoft Excel, complete tests, and research top gigs to craft high-ranking offerings.

learn to create a professional Fiverr gig for data analysis in Excel, including category and pricing tiers. set delivery times, attach files and media, and define basic, standard, premium packages.

Promote your Fiverr gig by sharing the link via the mobile app, posting in data analysis groups on Facebook, Twitter, YouTube, and LinkedIn, and engaging with clients through inbox messages.

Explore one-way ANOVA for comparing GPA across three income categories, examining assumptions of normality, equal variances, and independence, using the F-test and p-values in Excel.

Analyze cgpa across low, medium, and high income groups in excel using a single-factor anova. Reveal f value 1.38 and p value 0.28, indicating no significant difference.

Students' GPA across high, medium, and low income groups shows no significant differences in a one-way ANOVA (F(2,12)=1.39, p>0.05); report descriptive means and standard deviations in an APA style table.

Examine n-way anova (without replication) and two-way anova to analyze the effects of income and gender on cgpa. Apply concepts of single vs multiple independent variables and replication in Excel.

Apply n-way ANOVA with replication to test two independent variables on a continuous outcome. Identify interaction effects and use replication to compare scores across schools and subjects.

Explore anova without replication in excel using a two-factor design, interpreting gender and income group effects on GPA, with descriptive and inferential statistics and APA-style reporting.

Analyzes a two-factor with replication ANOVA in Excel to compare school scores across Bangla, English, and statistics, showing school two higher and no significant subject or interaction effects.

Explore two-way between-group ANOVA results on gender and income effects on CGP, including APA-style reporting, descriptive statistics, and notes on ANOVA with and without replication.

Explore correlation analysis using Pearson's r to assess linear relationships between interval or ratio variables, interpret strength and direction, and test significance and p-values with t-tests in Excel.

Explore how to perform correlation in excel to assess the relationship between study hours and GPA, interpreting R=0.84, R², t-test, p-value, and population significance.

Explain reporting correlation analysis in APA format with Pearson r, showing a strong positive relation between study hours and GPA (r=0.84), t=5.59, p<0.001.