netrashetty
Netra Shetty
Autodesk, Inc. (NASDAQ: ADSK) is an American multinational corporation that focuses on 2D and 3D design software for use in architecture, engineering and building construction, manufacturing, and media and entertainment. Autodesk was founded in 1982 by John Walker, a coauthor of early versions of the company's flagship CAD software product AutoCAD, and twelve others. It is headquartered in San Rafael, California.
Autodesk became best-known for its flagship computer-aided design software AutoCAD. In addition to AutoCAD, Autodesk develops Digital Prototyping solutions[4] to visualize, simulate, and analyze real-world performance using a digital model during the design process. The company also develops Building Information Modeling software to generate and manage building data using a three-dimensional building model. Autodesk also provides digital media creation and management software from film and television visual effects, color grading, and editing to animation, game development, and design visualization
= Sum of squared Y score
The previous formula used in computing correlation coefficient standardizes the values. Therefore no matter what changes in scale or units of measurement are given it will not affect its value. For this cause, the correlation coefficient is frequently more helpful than a graphical representation in evaluating the strength of the relationship between two variables.
Aside from this, if the correlation index of the calculated rxy is not perfect, then it is recommended to use the following classifications (Guilford & Fruchter, 1973):
rxy Indication
between ± 0.80 to ± 1.00 : High Correlation
between ± 0.60 to ± 0.79 : Moderately High Correlation
between ± 0.40 to ± 0.59 : Moderate Correlation
between ± 0.20 to ± 0.39 : Low Correlation
between ± 0.01 to ± 0.19 : Negligible Correlation
Chi-square Analysis[2]
As indicated in the paper of Guilford & Fruchter, (1973), the indicated formula below is utilized to assess Ho for all forms of Chi-Square tests:
Where: fo = observed frequency in the cell.
fe = expected frequency in the cell (if Ho was true).
Note: the fe values for a no preference Chi-Square will always equal each other. The fe values for the no difference from an alternate population are specified by the Ho.
To determine fe for the no preference version of the Goodness of Fit Chi-Square, use the following formula:
The fe for the no difference from an alternate population are based on information provided about the alternative population. On the other hand, the degrees of freedom for a one-way Chi-Square is k-1, where k is the number of cells (i.e, the number of levels) in the design.
Basically, Chi-square is the most commonly reported non-parametric statistic. It can be used with one or more groups. It compares the actual number (freuquency) in each group with the expected number. The expected number can be based on theory, experience, or, or comparison groups. The question is whether the expected number differs significantly from the actual number. Chi-square is used when the data are nominal (categorical).
With regards to chi-square statistics, there are four assumptions that should be considered:
Frequency of Data
Adequate sample size
measures independent of each other
Theoretical basis for the categorization of the variables
The first assumption is that the data are frequency data, that is, a count of the number of subjects in each condition under analysis. The chi-square cannot be used to analyze the difference between scores or their means. If the data are not categorical, they must be categorized before being used. Whether to categorised depends on the data and the question to be answered.
The second assumption is that the sample size is adequate. In cross tabulation procedures, cells are formed by the combination of measures. None of the cells should be empty. Expected frequencies of less than five in 2 x 2 tables present problems. In larger tables, many researchers use the rule of thumb that not more than 20% of the cells should have frequencies of less than five (SPSS, 1999; p. 67). If the cells do not contain adequate numbers, then the variables should be restructured to have fewer categories.
The third assumption is that the measures are independent of each other. This means that categories created are mutually exclusive; that is, no subject can be in more than one cell in the design, and no subject can be used more than once. It also means that the response of one subject cannot influence the response of another.
The fourth assumption is that there is some theoretical reason for the categories. This ensures that analysis will be meaningful and prevents “fishing expeditions.” The latter could occur if the researcher kept recategorizing subjects, hoping to find relationship between variables. Research questions and methods for analysis are established before data collection. Although these may be modified to suit the data actually obtained, the basic theoretical structure remains.
Limitations of the Study
The broad scope of the research topic is the foremost concern when it comes to the identifiable limitations and barriers of the research activity but the holistic approach likewise will enable the researcher to contribute fully to the academe as well as to the involved business industries that will be analyzed. Meanwhile, when it comes to the selected data gathering procedures, limitations and shortcomings could be expected in the data gathering procedures. Beginning from the sampling techniques that will be used to identify the participants of the study up to the facilitation of the survey forms and the interview sessions, difficulties will be expected. But with the support of the academe to the research activity and the explanations that will be provided regarding the important contributions of the study to the business industry upon completion, will serve as gateway to the successful research accomplishment. Other political, economic, as well as social issues and constraints are also expected while conducting the research activity which will be evident in the different stakes of the participants and the companies they represent.
Autodesk became best-known for its flagship computer-aided design software AutoCAD. In addition to AutoCAD, Autodesk develops Digital Prototyping solutions[4] to visualize, simulate, and analyze real-world performance using a digital model during the design process. The company also develops Building Information Modeling software to generate and manage building data using a three-dimensional building model. Autodesk also provides digital media creation and management software from film and television visual effects, color grading, and editing to animation, game development, and design visualization
= Sum of squared Y score
The previous formula used in computing correlation coefficient standardizes the values. Therefore no matter what changes in scale or units of measurement are given it will not affect its value. For this cause, the correlation coefficient is frequently more helpful than a graphical representation in evaluating the strength of the relationship between two variables.
Aside from this, if the correlation index of the calculated rxy is not perfect, then it is recommended to use the following classifications (Guilford & Fruchter, 1973):
rxy Indication
between ± 0.80 to ± 1.00 : High Correlation
between ± 0.60 to ± 0.79 : Moderately High Correlation
between ± 0.40 to ± 0.59 : Moderate Correlation
between ± 0.20 to ± 0.39 : Low Correlation
between ± 0.01 to ± 0.19 : Negligible Correlation
Chi-square Analysis[2]
As indicated in the paper of Guilford & Fruchter, (1973), the indicated formula below is utilized to assess Ho for all forms of Chi-Square tests:
Where: fo = observed frequency in the cell.
fe = expected frequency in the cell (if Ho was true).
Note: the fe values for a no preference Chi-Square will always equal each other. The fe values for the no difference from an alternate population are specified by the Ho.
To determine fe for the no preference version of the Goodness of Fit Chi-Square, use the following formula:
The fe for the no difference from an alternate population are based on information provided about the alternative population. On the other hand, the degrees of freedom for a one-way Chi-Square is k-1, where k is the number of cells (i.e, the number of levels) in the design.
Basically, Chi-square is the most commonly reported non-parametric statistic. It can be used with one or more groups. It compares the actual number (freuquency) in each group with the expected number. The expected number can be based on theory, experience, or, or comparison groups. The question is whether the expected number differs significantly from the actual number. Chi-square is used when the data are nominal (categorical).
With regards to chi-square statistics, there are four assumptions that should be considered:
Frequency of Data
Adequate sample size
measures independent of each other
Theoretical basis for the categorization of the variables
The first assumption is that the data are frequency data, that is, a count of the number of subjects in each condition under analysis. The chi-square cannot be used to analyze the difference between scores or their means. If the data are not categorical, they must be categorized before being used. Whether to categorised depends on the data and the question to be answered.
The second assumption is that the sample size is adequate. In cross tabulation procedures, cells are formed by the combination of measures. None of the cells should be empty. Expected frequencies of less than five in 2 x 2 tables present problems. In larger tables, many researchers use the rule of thumb that not more than 20% of the cells should have frequencies of less than five (SPSS, 1999; p. 67). If the cells do not contain adequate numbers, then the variables should be restructured to have fewer categories.
The third assumption is that the measures are independent of each other. This means that categories created are mutually exclusive; that is, no subject can be in more than one cell in the design, and no subject can be used more than once. It also means that the response of one subject cannot influence the response of another.
The fourth assumption is that there is some theoretical reason for the categories. This ensures that analysis will be meaningful and prevents “fishing expeditions.” The latter could occur if the researcher kept recategorizing subjects, hoping to find relationship between variables. Research questions and methods for analysis are established before data collection. Although these may be modified to suit the data actually obtained, the basic theoretical structure remains.
Limitations of the Study
The broad scope of the research topic is the foremost concern when it comes to the identifiable limitations and barriers of the research activity but the holistic approach likewise will enable the researcher to contribute fully to the academe as well as to the involved business industries that will be analyzed. Meanwhile, when it comes to the selected data gathering procedures, limitations and shortcomings could be expected in the data gathering procedures. Beginning from the sampling techniques that will be used to identify the participants of the study up to the facilitation of the survey forms and the interview sessions, difficulties will be expected. But with the support of the academe to the research activity and the explanations that will be provided regarding the important contributions of the study to the business industry upon completion, will serve as gateway to the successful research accomplishment. Other political, economic, as well as social issues and constraints are also expected while conducting the research activity which will be evident in the different stakes of the participants and the companies they represent.
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