Atari is a corporate and brand name owned by several entities since its inception in 1972. It is currently owned by Atari Interactive, a wholly owned subsidiary of the French publisher Atari, SA (ASA).[1] The original Atari Inc. was founded in 1972 by Nolan Bushnell and Ted Dabney. It was a pioneer in arcade games, home video game consoles, and home computers. The company's products, such as Pong and the Atari 2600, helped define the computer entertainment industry from the 1970s to the mid 1980s.
In 1984, the original Atari Inc. was split, and the arcade division was turned into Atari Games Inc.[2] Atari Games received the rights to use the logo and brand name with appended text "Games" on arcade games, as well as rights to the original 1972 - 1984 arcade hardware properties. The Atari Consumer Electronics Division properties were in turn sold to Jack Tramiel's Tramel Technology Ltd., which then renamed itself to Atari Corporation.[3][4] In 1996, Atari Corporation reverse merged with disk drive manufacturer JT Storage (JTS),[5] becoming a division within the company.


There’s no question that the vast majority of marketers are shifting more spending to digital, and a combination of tried-and-true tactics with newer online options will benefit from increasing budgets.
But advertisers and their agencies are not yet on the same page when it comes to the details of many of those changes.

According to a report on 2011 marketing budgets from Econsultancy and SAS, agencies worldwide are more eager than their clients to increase spending on newer digital marketing tactics, while advertisers show a greater interest in upping budgets for the time-tested.

For example, agencies were 13 percentage points more likely than advertisers to say their clients would be increasing mobile marketing spending.

Advertisers were out in front of their agencies with reports of spending increases for email marketing, corporate websites, paid search and display ads.



US-based research from the Direct Marketing Association (DMA) found similar patterns. Marketers were more likely than agencies to say they always or often used online tactics like emails, paid search, SEO and display.

Agencies, as in the Econsultancy data, placed a significantly greater emphasis on mobile; they were 7 percentage points more likely than marketers to be familiar with it, and more than twice as likely to use it frequently.



The Econsultancy study also found agencies and their clients disagreed about their ability to measure the return investment from many digital channels.
Advertisers were more optimistic than agencies about how well they could assess the success of their efforts with paid search, email, corporate websites, display and mobile.



Whether advertisers are overconfident or agencies too tough on their clients’ capabilities, the perceptual gap could be significant in determining which channels see more of clients’ dollars.

With respect to this model, the data gathered will be assessed through the help of Microsoft excel and a statistical software called SPSS. Basically, the SPSS software will be used to validate the hypothesis. Thus, descriptive statistics, correlation and Chi-square will be run in SPSS software.

Descriptive Statistics

In the descriptive statistics, it is likely that the study will be inexpensive and swift. It can also propose unexpected hypotheses. Nevertheless, this statistics will be very firm to rule out different clarifications and principally deduce causations. This descriptive statistics utilizes observations in the study. In descriptive statistics measures of central tendency (e.g. mean, median, and mode) and measures of dispersion (e.g. standard deviation, range, variance) will be computed.



Correlation[1]

According to Guilford & Fruchter (1973), the strength of the linear association between two variables is quantified by the correlation coefficient. Since this paper is in quantitative approach which is also mainly limited to counting, coming up with frequency and cumulative distributions, and computations of percentages, then these methods of analysis yield facts and data, the uses are quite limited. Facts in and of themselves do not speech much, for instance, of achievement or performance are related to other factors that such phenomena are better understood, predicted and to some extent even controlled.

The basic purposes of sciences are description, explanation, prediction and control. Differences in a performance, for example, are better explained if other phenomena, events or even other performance are used to account for each difference. This is achieved through a process called correlation. In a sense t-tests and F-tests are special cases of correlation. Sometimes such relationship show cause-effect but sometimes it is just plain relationship.

Correlations may either be bivariate (at least) or multivariate. Actually, in this paper, the use of Pearson Product moment correlation is considered. The Pearson Product moment correlation is used if the purpose is to determine the relationship or co-variation between two variables that are usually of the interval type of data. Basically, there are two types of correlation depending on the nature of correlation. Correlation may either be positive or negative. Correlation is positive if the objects, items or cases who got high in one variable are also those who got high in another variable, and those who got low in one variable also got low in the other variable.

Correlation is negative if the reverse seems to be the pattern. That is, those who got high in one factor are generally the ones who got low in the other factor; those who got low in one factor got high in the other factor. Correlation, or r for short in the case of a Product Moment Correlation ranges from r = -1.00 to r = +1.00 as limiting values. If r = +1.00 nor r = -1.00. If the general pattern of scores indicates positive correlation or negative correlation, there are usually stray cases which do not fit the mold. These cases cause the correlation to be less than perfect, that is the r may range between, say r = .01 to r = .99 in the case of positive correlation; r =-.01 to r = -.99 in the case of negative correlation. The formula used in this type of statistic is:
 
Last edited:

jamescord

MP Guru
Atari is a corporate and brand name owned by several entities since its inception in 1972. It is currently owned by Atari Interactive, a wholly owned subsidiary of the French publisher Atari, SA (ASA).[1] The original Atari Inc. was founded in 1972 by Nolan Bushnell and Ted Dabney. It was a pioneer in arcade games, home video game consoles, and home computers. The company's products, such as Pong and the Atari 2600, helped define the computer entertainment industry from the 1970s to the mid 1980s.
In 1984, the original Atari Inc. was split, and the arcade division was turned into Atari Games Inc.[2] Atari Games received the rights to use the logo and brand name with appended text "Games" on arcade games, as well as rights to the original 1972 - 1984 arcade hardware properties. The Atari Consumer Electronics Division properties were in turn sold to Jack Tramiel's Tramel Technology Ltd., which then renamed itself to Atari Corporation.[3][4] In 1996, Atari Corporation reverse merged with disk drive manufacturer JT Storage (JTS),[5] becoming a division within the company.


There’s no question that the vast majority of marketers are shifting more spending to digital, and a combination of tried-and-true tactics with newer online options will benefit from increasing budgets.
But advertisers and their agencies are not yet on the same page when it comes to the details of many of those changes.

According to a report on 2011 marketing budgets from Econsultancy and SAS, agencies worldwide are more eager than their clients to increase spending on newer digital marketing tactics, while advertisers show a greater interest in upping budgets for the time-tested.

For example, agencies were 13 percentage points more likely than advertisers to say their clients would be increasing mobile marketing spending.

Advertisers were out in front of their agencies with reports of spending increases for email marketing, corporate websites, paid search and display ads.



US-based research from the Direct Marketing Association (DMA) found similar patterns. Marketers were more likely than agencies to say they always or often used online tactics like emails, paid search, SEO and display.

Agencies, as in the Econsultancy data, placed a significantly greater emphasis on mobile; they were 7 percentage points more likely than marketers to be familiar with it, and more than twice as likely to use it frequently.



The Econsultancy study also found agencies and their clients disagreed about their ability to measure the return investment from many digital channels.
Advertisers were more optimistic than agencies about how well they could assess the success of their efforts with paid search, email, corporate websites, display and mobile.



Whether advertisers are overconfident or agencies too tough on their clients’ capabilities, the perceptual gap could be significant in determining which channels see more of clients’ dollars.

With respect to this model, the data gathered will be assessed through the help of Microsoft excel and a statistical software called SPSS. Basically, the SPSS software will be used to validate the hypothesis. Thus, descriptive statistics, correlation and Chi-square will be run in SPSS software.

Descriptive Statistics

In the descriptive statistics, it is likely that the study will be inexpensive and swift. It can also propose unexpected hypotheses. Nevertheless, this statistics will be very firm to rule out different clarifications and principally deduce causations. This descriptive statistics utilizes observations in the study. In descriptive statistics measures of central tendency (e.g. mean, median, and mode) and measures of dispersion (e.g. standard deviation, range, variance) will be computed.



Correlation[1]

According to Guilford & Fruchter (1973), the strength of the linear association between two variables is quantified by the correlation coefficient. Since this paper is in quantitative approach which is also mainly limited to counting, coming up with frequency and cumulative distributions, and computations of percentages, then these methods of analysis yield facts and data, the uses are quite limited. Facts in and of themselves do not speech much, for instance, of achievement or performance are related to other factors that such phenomena are better understood, predicted and to some extent even controlled.

The basic purposes of sciences are description, explanation, prediction and control. Differences in a performance, for example, are better explained if other phenomena, events or even other performance are used to account for each difference. This is achieved through a process called correlation. In a sense t-tests and F-tests are special cases of correlation. Sometimes such relationship show cause-effect but sometimes it is just plain relationship.

Correlations may either be bivariate (at least) or multivariate. Actually, in this paper, the use of Pearson Product moment correlation is considered. The Pearson Product moment correlation is used if the purpose is to determine the relationship or co-variation between two variables that are usually of the interval type of data. Basically, there are two types of correlation depending on the nature of correlation. Correlation may either be positive or negative. Correlation is positive if the objects, items or cases who got high in one variable are also those who got high in another variable, and those who got low in one variable also got low in the other variable.

Correlation is negative if the reverse seems to be the pattern. That is, those who got high in one factor are generally the ones who got low in the other factor; those who got low in one factor got high in the other factor. Correlation, or r for short in the case of a Product Moment Correlation ranges from r = -1.00 to r = +1.00 as limiting values. If r = +1.00 nor r = -1.00. If the general pattern of scores indicates positive correlation or negative correlation, there are usually stray cases which do not fit the mold. These cases cause the correlation to be less than perfect, that is the r may range between, say r = .01 to r = .99 in the case of positive correlation; r =-.01 to r = -.99 in the case of negative correlation. The formula used in this type of statistic is:

Hello Netra,

It was really appreciable and i am sure it would help many people. Well, i found some important information Study Report on Atari and wanna share it with you and other's. So please download and check it.
 

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