For 100 companies, I have collected (i) `tweets` and (ii) corporate website `pageviews` for `148` days. The tweetvolume and pageviews per day are two independent variables corpaired against the stock `trading volume` for each company, resulting in 100 x 148 = 14,800 observations. My data is structured like this:

```
company  date  tweetVol  pageviewVol  tradingVol
------------------------------------------------
1        1     200        150          2423325
1        2     194        152          2455343
1        3     214        199          3100429
.        .      .          .              .
.        .      .          .              .
1       148    205        233          2563463
2        1     752        932          7434124
2        2     932       2423          7464354
2        3     600       1435          5324323
.        .      .          .              .
.        .      .          .              .
.        .      .          .              .
100      148     3         155           32324

```

Because there is much difference in company-size (some companies only receive 2 tweets per day, where others like Apple get over 10,000 per day), all variables are logged to smoothen distribution. (This is in line with previous research - this is for my thesis).

I just performed a linear regression on this data, including both independend variables. R-Squared is .411 but Durbin-Watson only .141 (!) Without looking for the exact bounderies, I know this directly means my residuals are non-linear, eg. auto-correlated, right?

My question is: how can I solve this? When I think about it, this data should not be autocorrelated, so I don't really understand. Is it due to this actually being a timeseries analysis? I wouldn't think that either, since for instance trading volume today is independent of yesterdays trading volume. Can somebody explain this to me?

P.S. At my university, we use SPSS/PASW without additional modules, so I am unable to perform a timeseries analysis on this like you could in STATA or R.