MS Office Question & Answer

 

Computer Fundamentals (CF)

Class#01 (Concept of IT)

 

 

1.     What is Secondary Memory and write some examples?

 Answer: Secondary Memory refers to the external storage device which can be used to store data or information permanently. Hard Disk, CD, DVD, USB Drive, Memory Card are some examples of secondary memory in computers.

 

2.     What is the full form CD & DVD?

 Answer: CD stands for Compact Disc. DVD stands for Digital Versatile Disc.

 

3.     What is the storage capacity of a CD & DVD?

 Answer: A CD can store 650 MB of data. A DVD can store over 4 GB of data.

 

4.     Write down some output device name.

Answer: Projection devices, Speakers and headphones, Speech synthesizers, Printers.

 

5.     Describe about Speech synthesizers.

Answer: Speech synthesizers is a recent development which has ability not only to display text on a monitor but also to read the text to user. Thus, a user could receive a text email from a colleague and the system could read that email to user. 

 

6.     Describe about Projection devices.

Answer: These are projection devices which can be attached to a computer and are useful for displaying presentations to a group of people. They are best used in combination with presentation programs, such as Microsoft PowerPoint. They are used within education and are also very popular for sales presentations.

 

7.     What is mobile phone? or cell phone? 

Answer: The Mobile phone is a portable electronic device that used for generally mobile communication, SMS for text messaging, email accessing and radio hearing etc.

 

8.     What is Smart phone? 

Answer: A smart phone is a portable device that combines mobile telephone and computing functions in one unit 

 

9.     What are the differences between mobile phone and smart phone? 

Answer: Mobile phone offer texting and calling functionality whereas Smart phone offer a list of features including email, Internet access, video chatting, gaming, app downloading, video talking music storage etc.

 

10.     System unit is the part of what?

Answer: Hardware

 

11. When the PC industry began?

Answer: 1977

 

12. What are the first off, the shelf computer as a consumer product?

Answer: Apple, Radio Shack, Commodore.

 

13. When IBM introduced their IBM PC?

Answer: 1981

 

1     What is hardware?

Answer: In general, Hardware are the things of a computer that can physically be touched. Actually, the term hardware refers to the physical components of a computer such as the system unit, mouse, keyboard, monitor etc.

 

1    How many bits are there in a byte?

 Answer: 8 bits

 

1    Which of the unit represents the largest amount of data?

Answer: Terabyte

 

1    Name some of the storage media?

Answer: CD, DVD, USB flash drive etc.

 

1    What is the main working memory used in the computer?

Answer: RAM

 

1    What does RAM stand for?

Answer: Random Access Memory

 

        Where is RAM located?

Answer: In mother board

 

2    What is the main task of input device?

Answer: Input device allow us to input information or command to the computer and include things such as keyboard or mouse.

 

        Write down some input device name

Answer: Keyboard, Mouse, Scanners, Tracker Ball, Touch Pad, Joysticks, Webcams, Microphones- These all are input device.

 

2    What is the full meaning of OCR?

Answer: Optical Character Recognition

 

2     What is the main task of OCR?

Answer: OCR is a specialist programs which are specifically designed for converting printed text into editable text within your application.

 

2        What is Software?

Answer: Software is The Collection of Instructions Which Makes the Computer Work.

 

         What is an Operating System?

Answer: The Operating System is a Special Type of Program Which Load Automatically when you start your computer.

 

        Write down the examples of application software.

Answer: Examples of application software are given below:

a)    Database

b)    Word Processing

c)    Spreadsheets

d)    Presentation

e)    Emailing

f)     Web Browsing

g)    Photo Editing

h)    Computer Games

 

2    What is mobile phone? or cell phone? 

Answer: The Mobile phone is a portable electronic device that used for generally mobile communication, SMS for text messaging, email accessing and radio hearing etc.

 

2    What is a Smart phone? 

Answer: A smart phone is a portable device that combines mobile telephone and computing functions in one unit.

 

    What is the differences between mobile phone and smart phone? 

Answer: Mobile phone offer texting and calling functionality whereas Smart phone offer a list of features including email, Internet access, video chatting, gaming, app downloading, video talking music storage etc.

 

3    What is web cam?

Answer: A webcam is a digital video device commonly built into a computer. It main function is to transmit picture over the internet.

 

     What is digital camera?

Answer: Digital camera is a hardware device that take photograph and store the image as data on a memory card.

 

3    What is microphone?

Answer: Microphone is a hardware device that translate sound vibration in the air into electronic signal or scribes them to a recording medium.

 

         What is the definition of CPU?

Answer: The central processing unit (CPU) is the principal part of any computer system, is generally composed of the control unit, Arithmetic Logic Unit (ALU) and, registers. It forms the physical heart of the entire computer system. The CPU is called the processor.

 

3     What is the full meaning of "CPU" and "ALU"?

Answer: Central Processing Unit (CPU) and, Arithmetic Logic Unit (ALU).

 

3    What is the primary component of a computer?

Answer:

a)    Central processing unit (CPU).

b)    Types of Memory.

c)    The hard disk.

d)    Input and output devices.

SPSS Tutorials: definition, uses, SPSS steps and interpretation of independent sample t-test.

         SPSS Tutorials: definition, uses, SPSS steps and interpretation of independent sample t-test.

                                                                            Md. Sharif Hossain 

What is Independent sample t-test

    The Independent-Samples t Test approach automates the estimation of the t test effect size while comparing the means for two groups of cases. In order to ensure that any differences in reaction are caused by the treatment (or lack of treatment) and not by other factors, the subjects for this test should ideally be randomly assigned to two groups. If you compare the average wage for men and women, this is not the case. The gender of a person is not chosen at random.

 When we use independent sample t-test 

The following are frequently put to the test using the independent samples t test:

• Statistical variations between two groups' means
• Comparison of the means of two interventions, with statistics
• Differences in two change scores' means based on statistics
•  

        It should be noted that the Independent Samples t Test can only compare the means of two groups. Comparisons between more than two groups are impossible. You should probably perform an ANOVA if you want to compare the means of more than two groups.

 

 

what Steps need to calculate Independent sample t-test by using IBM SPSS :

 

1.     From the menus choose:

Analyze > Group comparison - parametric > Independent-samples t test

2.     Click Select variables under the Dependent variables section and select one or more quantitative dependent variables. A separate t test is computed for each variable. Click OK after selecting the variables.

3.     Click Select variable under the Group variable section and select a single grouping variable. The variable can be numeric or string. Click OK after selecting the variable.

4.     Optionally, click the link next to the group variable to specify values for the groups that you want to compare, or to specify a cut point value. For more information, see Independent-samples t test: Define groups.

5.     Optionally, you can select the following options from the Additional settings menu:

o   Click Statistics to select which statistics to include in the analysis.

o   Click Options to set the confidence interval level and control the treatment of missing data.

o   Click Bootstrap for deriving robust estimates of standard errors and confidence intervals for estimates such as the mean, median, proportion, odds ratio, correlation coefficient or regression coefficient.

 

The variable(s) under consideration This is the continuous variable whose meaning will be compared between the two groups. You may run multiple t tests simultaneously by selecting more than one test variable.

 Grouping Variable:  The independent variable is grouped as such. The categories (or groups) of the independent variable will define which samples will be compared in the t test. The grouping variable must have at least two categories (groups); it may have more than two categories, but a category can only compare two groups, so you will need to specify which two groups to compare. You can also use a continuous variable by specifying a cut point to create two groups (i.e., values at or above the cut point and values below the cut point).

 Define Groups: Click Define Groups to define the category indicators (groups) to use in the t test. If the button is not active, make sure that you have already moved your independent variable to the right in the Grouping Variable field. You must define the categories of your grouping variable before you can run the Independent Samples t Test procedure.

 Options: The Options section is where you can set your desired confidence level for the confidence interval for the mean difference and specify how SPSS should handle missing values.

When finished, click OK to run the Independent Samples t Test, or click Paste to have the syntax corresponding to your specified settings written to an open syntax window. (If you do not have a syntax window open, a new window will open for you.)

 

 How to interpret the independent sample t-test 

OUTPUT

Tables

Two sections (boxes) appear in the output: Group Statistics and Independent Samples Test. The first section, Group Statistics, provides basic information about the group comparisons, including the sample size (n), mean, standard deviation, and standard error for mile times by group. In this example, there are 166 athletes and 226 non-athletes. The mean mile time for athletes is 6 minutes 51 seconds, and the mean mile time for non-athletes is 9 minutes 6 seconds.

The second section, Independent Samples Test, displays the results most relevant to the Independent Samples t Test. There are two parts that provide different pieces of information: (A) Levene’s Test for Equality of Variances and (B) t-test for Equality of Means.

A Levene's Test for Equality of of Variances: This section has the test results for Levene's Test. From left to right:

• F is the test statistic of Levene's test
• Sig. is the p-value corresponding to this test statistic.

The p-value of Levene's test is printed as ".000" (but should be read as p < 0.001 -- i.e., p very small), so we we reject the null of Levene's test and conclude that the variance in mile time of athletes is significantly different than that of non-athletes. This tells us that we should look at the "Equal variances not assumed" row for the t test (and corresponding confidence interval) results. (If this test result had not been significant -- that is, if we had observed p > α -- then we would have used the "Equal variances assumed" output.)

B t-test for Equality of Means provides the results for the actual Independent Samples t Test. From left to right:

• t is the computed test statistic, using the formula for the equal-variances-assumed test statistic (first row of table) or the formula for the equal-variances-not-assumed test statistic (second row of table)
• df is the degrees of freedom, using the equal-variances-assumed degrees of freedom formula (first row of table) or the equal-variances-not-assumed degrees of freedom formula (second row of table)
• Sig (2-tailed) is the p-value corresponding to the given test statistic and degrees of freedom
• Mean Difference is the difference between the sample means, i.e. x₁ − x₂; it also corresponds to the numerator of the test statistic for that test
• Std. Error Difference is the standard error of the mean difference estimate; it also corresponds to the denominator of the test statistic for that test

Note that the mean difference is calculated by subtracting the mean of the second group from the mean of the first group. In this example, the mean mile time for athletes was subtracted from the mean mile time for non-athletes (9:06 minus 6:51 = 02:14). The sign of the mean difference corresponds to the sign of the t value. The positive t value in this example indicates that the mean mile time for the first group, non-athletes, is significantly greater than the mean for the second group, athletes.

The associated p value is printed as ".000"; double-clicking on the p-value will reveal the un-rounded number. SPSS rounds p-values to three decimal places, so any p-value too small to round up to .001 will print as .000. (In this particular example, the p-values are on the order of 10^-40.)

C Confidence Interval of the Difference: This part of the t-test output complements the significance test results. Typically, if the CI for the mean difference contains 0 within the interval -- i.e., if the lower boundary of the CI is a negative number and the upper boundary of the CI is a positive number -- the results are not significant at the chosen significance level. In this example, the 95% CI is [01:57, 02:32], which does not contain zero; this agrees with the small p-value of the significance test.

---

DECISION AND CONCLUSIONS

Since p < .001 is less than our chosen significance level α = 0.05, we can reject the null hypothesis, and conclude that the that the mean mile time for athletes and non-athletes is significantly different.

Based on the results, we can state the following:

• There was a significant difference in mean mile time between non-athletes and athletes (t_315.846 = 15.047, p < .001).
• The average mile time for athletes was 2 minutes and 14 seconds lower than the average mile time for non-athletes.

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Descriptive Statistics Analysis With IBM SPSS

               Descriptive Statistics Analysis and          

                   Visualization With  IBM SPSS

                                                By Md. Sharif Hossain 

What Are Descriptive Statistics?

Brief informative coefficients known as descriptive statistics are used to sum up a particular data set, which may be a sample of a population or a representation of the complete population. In a nutshell, descriptive statistics provide brief summaries of the sample and data measurements to aid in describing and understanding the characteristics of a particular data set.

The mean, median, and mode, which are utilized at practically all math and statistics levels, are the most well-known types of descriptive statistics. Measurements of central tendency and measures of variability make up descriptive statistics (spread). The mean, median, and mode are measurements of central tendency, while the standard deviation, variance, minimum and maximum variables, kurtosis, and skewness are measures of variability.

What Is the Main Purpose of Descriptive Statistics?

Descriptive statistics are mostly used to provide details about a data set. The large amount of data is condensed into numerous helpful facts using descriptive statistics.

 

Types of Descriptive Statistics

Measure of Central Tendency

Measure of Variance

Measure of Variability

What are the Measure of central Tendency?

        A summary measure called a measure of central tendency, also known as a measure of center or a measure of central placement, aims to characterize the entirety of a set of data with a single number that corresponds to the middle or center of its distribution.

The mode, the median, and the mean are the three primary indicators of central tendency. The typical or core value in the distribution is indicated differently by each of these metrics.

 

What is the mean?

A dataset's mean is calculated by dividing the total value of all observations by the total number of observations. The arithmetic average is another name for this concept.

Taking another look at the distribution of retirement ages: 54, 54, 54, 55, 56, 57, 57, 58, 58, 60, 60,

When all of the values are added up (54+54+55+56+57+57+58+58+60+60 = 623) and divided by the number of observations (11), the mean is obtained as 56.6 years.

 

What is the median?

When values are organized in ascending or descending order, the median is the value that falls in the middle of the distribution.

There are 50% of observations on either side of the median value, which divides the distribution in half. The median value is the midpoint of a distribution with an odd number of observations.

The median, or middle value, is 57 years when examining the retirement age distribution, which comprises 11 observations: 54, 54, 54, 55, 56, 57, 57, 58, 58, 60, 60,

The median value is the mean of the two middle values when there are an equal number of observations in the distribution. The two middle values in the following distribution are 56 and 57, making the median age 56.5 years: 52, 54, 54, 54, 55, 56, 57, 57, 58, 58, 60, 60,

 

What is the mode?

In a distribution, the mode is the value that appears the most frequently. Take a look at this dataset, which displays the age at retirement in entire years for 11 people: 54, 54, 54, 55, 56, 57, 57, 58, 58, 60, 60,

 

Age	Frequency
54	3
55	1
56	1
57	2
58	2
60	2

The median age in this distribution is 54 years, which is also the value that occurs most frequently.

 

Measures of Variability

A collection of data's distribution's dispersion can be determined using measures of variability (also known as spread). For instance, while the central tendency measurements can provide a person with the average of a group of data, they cannot represent the distribution of the data within the set.

Range

We'll start with the range since it is the easiest to compute and comprehend of all the measures of variability. The difference between a dataset's largest and smallest values is known as its range. In the two datasets below, for instance, dataset 1's range is 20–38, or 18, and dataset 2's range is 11–52, or 41. Dataset 2 is more variable than Dataset 1 since it covers a wider range.

 

Dataset 1	Dataset 2
20	11
21	16
22	19
25	23
26	25
29	32
33	39
34	46
38	52

 

The range is simple to understand, but it is highly prone to outliers because it is based simply on the two most extreme values in the dataset. Even if it is out of the ordinary, if one of those values is extremely high or low, it has an impact on the entire range.

The range is also impacted by the amount of the dataset. Extreme values are often less likely to be seen. However, there are more chances to get these high results as the sample size grows.

Variance

Because the range is based just on the two most extreme values in the dataset, it is easy to interpret but very susceptible to outliers. Even though it is unusual, the entire range is affected if one of those numbers is very high or low.

 

The size of the dataset also affects the range. Extreme values are frequently harder to find. As the sample size increases, there are more opportunities to achieve extremely high outcomes.

 

Standard Deviation

The normal or average difference between each data point and the mean is known as the standard deviation. You have a reduced standard deviation when the values in a dataset are clustered more closely together. Conversely, when values are more dispersed, the standard deviation is higher because the standard deviation is higher.

 

The standard deviation conveniently uses the original units of the data, which simplifies interpretation. The standard deviation is therefore the most frequently employed measure of variability.

 

Distribution

The frequency distribution of a data point describes how frequently a data point appears. In contrast, it is the measurement of a data point not happening.

Univariate vs. Bivariate

Univariate data analysis in descriptive statistics uses just one variable. It does not evaluate any relationships or causes; rather, it is used to pinpoint traits that make up a single trait.

On the other hand, bivariate data makes an effort to link two variables together by looking for correlation. The relationship between the two forms of data is studied after they have both been acquired. This method may also be referred to as multivariate because several variables are examined.

 

Summary Statistics

Summary statistics are the most typical technique for carrying out univariate analysis. The degree of measurement or the type of data that the variables hold determines the relevant statistics. The two most popular kinds of summary statistics are as follows:

 

Measures of Dispersion

These figures show the degree to which values in a dataset are uniformly distributed. Examples include the variance, interquartile range, range, and standard deviation.

Range is the space between a dataset's highest and lowest values.

Standard Deviation: A typical way to gauge the spread

The range of values in the middle 50% is known as the interquartile range.

 

Frequency distribution table

Frequency refers to how frequently something occurs. The number of times an event occurs is revealed by the observation frequency. Variables that are categorical, qualitative, numerical, or quantitative may be displayed in the frequency distribution table. The distribution provides a snapshot of the data and enables pattern discovery.

Bar chart

Rectangular bars are used to depict the bar chart. Various categories will be compared in the graph. The graph might be displayed either vertically or horizontally. The bar will often be plotted vertically. The category will be represented by the horizontal or x-axis, and the category's value by the vertical or y-axis. The bar graph examines and contrasts the data set. It might be used, for instance, to determine which component consumes the most expenditure.

Histogram

The analysis of the data is important since the histogram functions similarly to a bar chart. The histogram divides the categories into bins, and the bar graph counts against the categories. The bin can display the range, the interval, or the number of data points.

 

Frequency Polygon

The histogram and the frequency polygon are quite similar. These can be utilized, nevertheless, to contrast the data sets or to show the cumulative frequency distribution. A line graph will serve as the representation for the frequency polygon.

Pie Chart

The data is shown in a circular layout on the pie chart. The graph is broken up into sections, each of which is proportional to the percentage of the entire category. Each pie slice in the pie chart is thus compared to the size of the category. Since the entire pie is 100 percent, adding up all of the pie slices should also result in a total of 100.

Pie charts are used to visualize how a group is divided into smaller components.

Classification Chart of Multivariate Techniques

Various factors determine which multivariate technique is most appropriate.

a)     Do the variables fit into the independent and dependent categories?

b) If so, how many variables in a single analysis are regarded as dependents?

c) What measurements are made for the dependent and independent variables?

Dependent and non-dependent categories can be used to group this multivariate analysis technique. The classification is based on whether or not the relevant variables are interdependent.

We have dependent techniques if the response is affirmative.

We have interdependence methods if the response is no.

Techniques for multivariate analysis that are employed when one or more of the variables can be classified as dependent variables and the other variables can be classified as independent are known as dependency techniques.

Multiple Regression

Analysis Using Multiple Regression: Multiple regression is a simple linear regression extension. When predicting the value of a variable based on the values of two or more other variables, this technique is employed. The dependent variable is the one we're trying to forecast (or sometimes, the outcome, target, or criterion variable). For each independent variable, multiple "x" variables are used in multiple regression: (x1)1, (x2)1, (x3)1, Y1)

 

Conjoint analysis  

‘Conjoint analysis ‘is a survey-based statistical technique used in market research that helps determine how people value different attributes (feature, function, benefits) that make up an individual product or service. The objective of conjoint analysis is to determine the choices or decisions of the end-user, which drives the policy/product/service.

Multiple Discriminant Analysis

By identifying linear combinations of the variables that maximize the differences between the variables under study, discriminant analysis aims to determine the group membership of samples from a set of predictors. This method creates a model that accurately sorts objects into the proper populations.

A linear probability model:

A regression model called a linear probability model (LPM) uses one or more explanatory factors to make predictions about a binary outcome variable. Explanatory variables themselves can be continuous or binary. It is advisable to employ a linear probability model when the dependent variable is a yes/no decision and some of the independent variables are not metric.

Multivariate Analysis of Variance and Covariance

A variation of the conventional analysis of variance is the multivariate analysis of variance (MANOVA) (ANOVA). ANOVA compares differences in group means for a single response variable.

Canonical Correlation Analysis

The study of the linear relationships between two sets of variables is known as canonical correlation analysis. It is the correlation analysis's multivariate extension.

There are two typical uses for CCA:

Data compression

Interpreting data

 

Structural Equation Modelling

A multivariate statistical analytic method called structural equation modeling is employed to examine structural relationships. It is a very comprehensive and adaptable framework for data analysis, and it may be more useful to think of it as a group of connected techniques than as a single one.

Interdependence Technique

When variables are related, they cannot be categorized as dependent or independent, which is the case with interdependence approaches.

Without making any explicit assumptions about the distributions of the variables, it seeks to reveal relationships between variables and/or people. Without making (very) firm assumptions about the variables, the goal is to describe the patterns in the data.

Factor Analysis 

Factor analysis is a method for reducing the amount of data in numerous variables to only a few. It also goes by the name "dimension reduction" for this reason. It results in a highly correlated set of variables.

Cluster analysis

Objects or cases are categorized into relative groupings called clusters using a range of techniques known as cluster analysis. In a cluster analysis, none of the objects have any prior knowledge of their group or cluster membership.

Multidimensional Scaling

A map showing the relative positions of various objects is produced using the multidimensional scaling (MDS) approach using simply a table of their respective distances. There could be one, two, three, or even more dimensions in the map.

Correspondence analysis                

In correspondence analysis, the rows and columns of a table of non-negative data are represented as points on a map to give them a specific spatial interpretation. Cross-tabulations typically count the data, but many other types of data can now be included as well with the proper data transformations.

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References

 

Statistical analysis in Psychology & Education, George A. Ferguson (6^th edition)

Statistical Techniques in Business & Economics, Douglas Lind (18^th edition)

https://www.investopedia.com/terms/d/descriptive_statistics.asp

https://www.abs.gov.au/websitedbs/D3310114.nsf/Home/Statistical+Language+-+measures+of+central+tendency

https://statisticsbyjim.com/basics/variability-range-interquartile-variance-standard-deviation/

https://www.jigsawacademy.com/blogs/business-analytics/univariate-analysis/

https://www.mygreatlearning.com/blog/introduction-to-multivariate-analysis/