bardzo proszę o pomoc:)nie małą. muszę przetłumaczyć tekst z angielskiego na polski, przetłumaczyłem trochę i proszę o sprawdzenie oraz o przetłumaczenie reszty bo nie dam rady, Z góry dziękuje za pomoc.
...wybranych spośród liczb od 000 do 999 itd.. Zauważ, że tabela wstępnie dzieli się na grupy po pięć tworząc ją łatwiejszą do odczytania. Ponieważ pełna liczba użytkowników telefonów komórkowych w planie 500-minutowym to cztero-cyfrowa liczba (2,136), losowo wyróżniamy pewne grupy czterech cyfr znajdujących się w tabeli (mamy je zakreślone kółkiem). Ta liczba, którą jest 0511 identyfikuje pierwszego przypadkowo wybranego użytkownika. Następnie przemieszczając się w dowolnym kierunku od liczby 0511 (do góry, na dół, w prawo, bądź w lewo - to nie jest istotne w którą stronę), wybieramy dodatkowe grupy cztero cyfrowe. Te kolejne grupy cyfr rozpoznają dalszych przypadkowo wyselekcjonowanych użytkowników telefonów komórkowych. Wtedy arbitralnie przesuwamy się od 0511 w dół tabeli. Pierwsze siedem grup czterech cyfr otrzymamy jako
[tel]
(Zobacz Tabelę 1.1(a)- te liczby zawierają się w prostokącie.). Ponieważ nie ma tam użytkowników oznaczonych jako 7156, 4461, 3990 lub 4919 (pamiętaj tylko 2,136 użytkowników jest w pakiecie 500- minutowym ), ignorujemy te liczby . To sugeruje, że trzema pierwszymi przypadkowo wybranymi użytkownikami są te numery 0511, 0285 i 1915. Kontynuując tę procedurę możemy otrzymać pełny, przypadkowy przykład 100 użytkowników telefonów komórkowych. Zauważ, że ponieważ próbkujemy bez zastępowania, powinniśmy ignorować pewne grupy czterech cyfr uprzednio wybieranych spośród dowolnych numerów tabeli.
Kiedy używamy przypadkowej liczby z tabeli to jest to jedna z dróg by wybrać przypadkowy przykład, ten zwrot ma wadę, że jest objaśniony na podstawie bieżącej sytuacji. Odmiennie od wielu cztero-cyfrowych liczb, przypadkowe liczby nie są pomiędzy 0001 i 2136, uzyskując 100 różnych kombinacji czterech cyfr przypadkowych liczb z pomiędzy 0001 i 2136 będą wymagały ignorowania dużej liczby przypadkowych liczb i bez wątpienia będziemy musieli użyć tabeli przypadkowych liczb, która jest większa niż tabela 1.1(a). Ponadto, większa tabela przypadkowych liczb jest łatwo dostępna w książkach, w tablicach matematycznych i statystycznych, dobrą alternatywą jest też użycie komputerowego pakietu programowego, który może generować przypadkowe liczby, które są pomiędzy jakimikolwiek wielkościami jakie określimy. Na przykład, Tabela 1.1(b) podaje MINITAB produkcji 100 różnych 4-cyfrowych przypadkowych liczb, które są pomiędzy 0001 i 2136 (zauważ, że pierwsze zera nie są wliczane do tych 4-cyfrowych liczb). Jeśli użyto, przypadkowych liczb w Tabeli 1.1(b) identyfikujących 100 pracowników to powinno to uczynić z przypadkowych prób.
tyle przetłumaczyłem i bardzo proszę o przetłumaczenie reszty:) z góry dziękuje:)
selected from the numbers 000 to 999, and so forth. Note that the table entries are segmented into groups of five to make the table easier to read. Because the total number of cell phone users on the 500-minute plan (2,136) is a four-digit number, we arbitrarily select any set of four digits in the table ( we have circled these digits). This number, which is 0511, identifies the first randomly selected user. Then, moving in any direction from the 0511 (up, down, right, or left- it does not matter which), we select additional sets of four digits. These succeeding sets of digits identify additional randomly selected users. Here we arbitrarily move down from 0511 in the table. The first seven sets of four digits we obtain are
[tel]
( See Table 1.1(a)-these numbers are enclosed in a rectangle.) Since there are no users numbered 7156, 4461, 3990, or 4919 (remember only 2,136 users are on the 500-minute plan), we ignore these numbers. This implies that the first three randomly selected users are those numbered 0511, 0285, and 1915. Continuing this procedure, we can obtain the entire random sample of 100 users. Notice that, because we are sampling without replacement, we should ignore any set of four digits previously selected from the random number table.
While using a random number table is one way to select a random sample, this approach has a disadvantage that is illustrated by the current situation. Specifically, since most four-digit random numbers between 0001 and 2136, obtaining 100 different, four-digit random numbers between 0001 and 2136 will require ignoring a large number of random numbers in the random number table, and we will in fact need to use a random number table that is larger then Table 1.1(a). Although larger random number tables are readily available in books of mathematical and statistical tables, a good alternative is to use a computer software package, which can generate random numbers that are between whatever values we specify. For example, Table 1.1(b) gives the MINITAB output of 100 different, four-digit random numbers that are between 0001 and 2136 (note that the “leading 0's" are not included in these four digit numbers). If used, the random numbers in Table 1.1(b) identify the 100 employees that should from the random sample.
proszę o przetłumaczenie tekstu poniżej:)bo ja już ledwo dyszę...
After the random sample of 100 employees is selected, the number of cellular minutes used by each employee during the month (the employee's cellular usage ) is found and recorded. The 100 cellular-usage figures are given in Table 1.2. Looking at this table, we can see that there is substantial overage and underage- many employees used far more than 500 minutes, while many others failed to use all of the 500 minutes allowed by their plan. In Chapter 2 we will use these 100 usage figures to estimate the cellular cost per minute for the 500-minute plan.
Approximately random samples In general, to take a random sample we must have a list, or frame, of all the population units. This needed because we must be able to number the population units in order to make random selections from them (by, for example, using a random number table). In Example 1.1, where we wished to study a population of 2,136 cell phone users who were on the bank's 500-minute cellular plan, we were able to produce a frame (list) of the population units. Therefore, we were able to select a random sample. Sometimes, however, it is not possible to list and number all the units in a population. In such a situation we often select a systematic sample, which approximates a random sample.
EXAMPLE 1.2 The Marketing Research Case: Rating a New Bottle Design
The design of a package or bottle can have an important effect on a company's bottom line. For example, an article in September 16,2004, issue of USA Today reported that the introduction of a contoured 1.5-liter bottle for Coke drinks (including the reduced-calorie soft drink Coke C2) played a major role in Coca-Cola's failure to met third-quarter earnings forecasts in 2004. According to the article, Coke's biggest bottler, Coca-Cola Enterprises, “said it would miss expectations because of the 1.5-liter bottle and the absence of common 2-liter and 12-pack sizes for C2 in supermarkets."
In this case a brand group is studying whether changes should be made in the bottle design for a popular soft drink. To research consumer reaction to a new design, the brand group will use the “mall intercept method" in which shoppers at a large metropolitan shopping mall are intercepted and asked to participate in a consumer survey. Each shopper will be exposed to the new bottle design and asked to rate the bottle image. Bottle image will be exposed to the new bottle and asked to rate the bottle image. Bottle image will be measured by combining consumers" responses to five items, with each response measured using a 7-point “Likert scale." The five items and the scale of possible responses are shown in Figure 1.1. Here, since we describe the least favorable response and the most favorable response (and we do not describe the responses between them), we say that the scale is “anchored" at its ends. Responses to the five items will be summed to obtain a composite score for each respondent. It follows that the minimum composite score possible is 5 and the maximum composite score possible is 35. Furthermore, experience has shown that the smallest acceptable composite score for a successful bottle design is 25.
In this situation, it is not possible to list and number each and every shopper at the mall while the study is being conducted. Consequently, we cannot use random numbers (as we did in the cell phone case) to obtain a random sample of shoppers. Instead, we can select a systematic sample. To do this, every 100th shopper passing a specified location in the mall will be invited to participate in the survey. Here, selecting every 100th shopper is arbitrary-we could select every 200th, every 300th, and so forth. By selecting every 100th shopper, it is probably reasonable to believe that the sampled shoppers obtained by the systematic sampling process make up an approximate random sample.
During a Tuesday afternoon and evening, a sample of 60 shoppers is selected by using the systematic sampling process. Each shopper is asked to rate the bottle design by responding to the five items in Figure 1.1, and a composite score is calculated for each shopper. The 60 composite scores obtained are given in Table 1.3. Since these scores range from 20 to 35, we might infer that most of the shoppers at the mall on the Tuesday afternoon and evening of the study would rate the new bottle design between 20 and 35. Furthermore, since 57 of the 60 composite scores are at least 25, we might estimate that the proportion of all shoppers at the mall on the Tuesday afternoon and evening who would give the bottle design a composite score of at least 25 is 57/60=.95. That is, we estimate that 95 percent of the shoppers would give the bottle design a composite score of at least 25.
In Chapter 2 we will see how to estimate a typical composite score and we will further analyze the composite scores in Table 1.3.
In some situations, we need to decide whether a sample taken from one population can be employed to make statistical inferences about another, related population. Often logical reasoning is used to do this. For instance, we might reason that the bottle design ratings given by shoppers at the mall on the Tuesday afternoon and evening of the research study would be representative of the ratings given by (1) shoppers at the same mall at other times, (2) shoppers at other malls, and (3) consumers in general. However, if we have no data or other information to back up this reasoning, making such generalizations is dangerous. In practice, marketing research firms choose locations and sampling times that data and experience indicate will produce a representative cross-section of consumers. To simplify our presentation, we will assume that this has been done in the bottle design case. Therefore, we will suppose that it is reasonable to use the 600 bottle design ratings un Table 1.3 to make statistical inferences about all consumers.
To conclude this section, we emphasize the importance of taking a random (or approximately random) sample. Statistical theory tells us that, when we select a random (or approximately random) sample, we can use the sample to make valid statistical inferences about the sampled population. However, if the sample is not random, we cannot do this. A classic example occurred prior to the presidential election of 1936, when the Literary Digest predicated that Alf Landon would defeat Franklin D. Roosevelt by a margin of 57 percent to 43 percent. Instead, Roosevelt won the election in a landslide.